Every product began with a real problem.

They cloned your business. We catch them in the act. Omega Guard hunts the AI-driven website clones built to defraud your customers and hijack your reputation, capturing law-enforcement-grade evidence while the crime is still in progress.

Why we built it

Law enforcement wants to act. The obstacle is rarely will. It is capacity. Chronic understaffing, limited training in cyber-enabled fraud, and layers of bureaucracy mean fraud reports stack up faster than investigators can ever work them. As a law enforcement officer, I have felt that frustration firsthand: the drive to serve and protect, held back by roadblocks that have nothing to do with the victim in front of me.

Omega Guard was built to clear those roadblocks. It gives the people who serve exactly what they need, delivered to exactly who needs it, the moment a fraud is detected. It is one piece of a larger mission, strengthening America from the bottom up. Small businesses are the backbone of the economy, and protecting them affordably and effectively, alongside the larger institutions Americans rely on every day, is how we build a more fraud-resistant country.

The problem it confronts

A clone attacks on three fronts at once.

• The customer is defrauded by a fake that looks exactly like a business they already trust.
• The business has its credibility hijacked and its hard-won online reach turned into a liability.
• The economy loses a little more of the small-business engine every time a brand is cloned.

How it works, the full detection engine

One clone leaves dozens of fingerprints. Omega Guard never relies on a single tell. It correlates signals across six layers, then a human confirms before anything is acted on.

1. Lookalike discovery. Domain permutation generation (dnstwist-style typo, homoglyph, and TLD-swap variants), a real-time certificate-transparency log tailer that reads every new TLS certificate the moment it is issued, newly-registered-domain feeds filtered to your brand, and a reverse tracking-ID pivot that finds sites reusing your Google Analytics, Tag Manager, Meta Pixel, or AdSense IDs.

2. Content and visual matching. Favicon hashing (Shodan-compatible mmh3), structural HTML fuzzy-hashing, perceptual screenshot similarity, and a content fingerprint that compares text, logo hashes, analytics IDs, and social handles to your verified baseline.

3. Clone-tool provenance. Signatures left by site-rippers (HTTrack, SingleFile, "saved from url", base-href rewrites, inlined assets), plus hotlink-back detection when a clone serves images straight from your servers.

4. Infrastructure and reputation. Domain age and registrar over RDAP, IP and ASN over Team Cymru, hosting-network reputation over GreyNoise (known-malicious networks raise the score, known-benign ones suppress false positives), and domain control records.

5. Early-warning sensors on your side. A clone canary beacon that phones home the instant your code, pixel, or favicon is copied; DNS-watch for drift, hijack, dangling-CNAME subdomain takeover, and missing CAA records; canary tokens; and a recon classifier that flags clone-tool user agents probing your site.

6. Correlation, triage, and accuracy. Detections that share indicators are clustered into rings so one operator's whole network surfaces together. Each candidate is scored automatically, then reviewed by a human before any action. Redirect-to-owner and known-benign-infrastructure rules keep legitimate sites from being flagged.

The result is early warning, not after-the-fact. A fake can be flagged as it is being stood up, often before it serves a single customer. It installs in minutes on any platform: a copy-paste snippet, a downloadable Cloudflare edge sensor, or a no-code WordPress plugin.

When a clone is found, consent-driven and evidence-grade

Omega Guard never acts behind your back. With your consent, every detection follows current evidentiary procedure: detect and flag the clone while capturing evidence, present the findings for your decision, compile a complete cyber-forensic log, alert FraudTrax for law-enforcement access only so investigators get the who, what, when, where, and how of a fraud in progress, and report the package to IC3 and the appropriate jurisdictions.

What makes it different

Automation finds the signals; people make the decisions. A real human-in-the-loop review keeps analysts in control of the calls that matter. Account and member verification lets genuine businesses stand apart from the impostors riding on their reputation, giving customers a way to confirm it is really you. Built and operated by Omega Point Solutions LLC, a Service-Disabled Veteran-Owned Small Business.

A bad actor rarely hits just one target. Omega Threat ID turns that into a defense: a community bad-actor IP registry where every member sharpens the list for all of them. Join free for the shared feed, or run a one-dollar sensor and your own network becomes part of the early-warning grid.

Why we built it

Fraud and intrusion are volume crimes. The same host that scans one small business is usually scanning hundreds, yet each victim sees only its own slice and reports into a silo, if it reports at all. Omega Threat ID was built to break that isolation. It pools what individual defenders see into one corroborated list, so a signal caught on one member's edge becomes protection for the whole community before the next target is hit.

The problem it confronts

Single-source blocklists are noisy. They flag shared infrastructure, they go stale, and they cannot tell a research scanner from an attacker. A defender acting on one feed alone ends up either blocking customers or missing real threats. The fix is corroboration: an address that shows up as hostile across several independent sources, and across several members' own sensors, is a very different thing from a one-off hit.

How it works

• Cross-source corroboration. Each candidate IP is checked against multiple independent intelligence sources, and only a fused verdict, not a lone data point, drives the listing and its confidence score.
• Community feed. Free members pull the shared list of corroborated bad actors and use it to inform their own blocking and triage.
• One-dollar sensor. Members can run a lightweight sensor that reports probing and abuse it observes. That signal both sharpens the community list and raises the accuracy of the member's own Omega Guard, FraudTrax, and Hatchet Trace verdicts.
• Fails closed. When corroboration is missing the system withholds a verdict rather than guessing, so the list stays trustworthy.

What makes it different

It is a network effect pointed at defense. Every member who joins, and especially every member who runs a sensor, makes the list better for everyone else, and the same corroborated intelligence flows straight back into the rest of the Omega Point suite. Built and operated by Omega Point Solutions LLC, a veteran-owned small business.

Transparency and limits

Omega Threat ID surfaces corroborated indicators, not proof of wrongdoing. A listing means an address is behaving like a bad actor across multiple sources, and it is meant to inform a human decision about blocking, rate-limiting, or investigation, never to act on its own.

Enterprise security operations centers watch the network around the clock and cost more than most organizations will ever spend. Omega Point DTX puts that capability on your own hardware: an AI layer that reads your security alerts, separates real threats from noise, tunes itself as your environment changes, and hardens the machine underneath, without your data ever leaving the building.

Why we built it

Most organizations that deploy intrusion detection abandon it within months, not because it misses threats but because it buries the one real alert under a thousand false positives. Triage is the expensive part, and it is exactly the part small teams cannot staff. DTX was built to do that triage the way a seasoned analyst would: read the alert, check the intelligence, recall what this machine normally does, and either clear it with a written explanation or escalate it with evidence.

The problem it confronts

Alert fatigue is a security vulnerability. When everything is urgent, nothing is, and the real intrusion scrolls past unread. The fix is not a louder dashboard. It is a system that honestly clears the noise, shows its work, and proposes the tuning a human approves, so the alerts that remain are the ones worth waking up for.

How it works

• Grounded AI triage. A local model reads each alert grounded by a curated security knowledge base and your own operational history, then renders a verdict with a plain-language explanation: benign false positive, real threat, or needs human review.
• Live threat intelligence. Alerts with a public source address are checked against the Omega Threat ID community registry and corroborated intelligence sources, so a login attempt from known hostile infrastructure escalates itself.
• Predictive tuning. A forecasting layer watches alert volume and sensor-observed attack classes, flags emerging spikes early, and pre-weights triage before the storm arrives.
• Human-gated suppression. When the AI judges an alert class benign it writes the exact suppression rule an analyst would, but nothing is applied until a human reviews it, and every applied change is validated and reversible.
• Posture hardening. An opt-in module scores the host against industry benchmarks, proposes fixes with rollback steps, and treats any regression from your approved baseline as a detection event.

What makes it different

DTX does not reinvent the detection engine. It rides on a battle-tested open platform trusted by enterprises worldwide, and adds the layer that platform is missing: judgment. Every verdict is explained, every suppression is auditable, and the whole loop runs on hardware you control. Built and operated by Omega Point Solutions LLC, a veteran-owned small business.

Transparency and limits

DTX renders verdicts to inform a human decision, not to replace one. Suppressions and hardening changes require explicit approval, uncertain alerts are marked for review rather than guessed at, and the system never acts on your infrastructure behind your back.

FraudTrax is an analysis engine made for fraud and auto-theft investigation by law enforcement. It reads the evidence in front of you, surfaces what a specialist in each vertical would catch, and ties every flag back to an authority. Fast enough for the curb, accurate enough for the courthouse.

Why we built it

FraudTrax is not a SaaS product dressed up to look friendly to law enforcement. It is a toolkit built from years of running the same temp-tag stops, dealer audits, and theft scenes that officers run every shift. The team came to software engineering from policing for one reason: technology accelerates crime faster than agencies can keep up, and affordable, solid tooling is the missing piece. FraudTrax is the answer to that gap, built by people who have stood on both sides of the problem.

The problem it confronts

The pattern recognition that catches a washed title or a fake temporary tag takes years of cases to build, and it usually lives in one or two veteran investigators per region. When they retire, it walks out the door. Meanwhile the answer an officer needs at the curb (is this VIN consistent, does this dealer lot reconcile, what authority does this violate) often takes days to assemble by hand, if it gets assembled at all. FraudTrax puts that specialist instinct in the officer's hand in seconds, and writes it down in language a court will accept.

How it works

DAN-O reads photos and structured case input and returns articulable indicators. For each one it cites the specific authority it violates (a registry record, a federal advisory, a statute, or a vehicle standard), names what cannot be determined from the evidence, and recommends the next step (NCIC, NMVTIS, NICB, hold). It then drafts a court-ready paragraph the officer can paste into the narrative. The officer signs off. The citation carries through to the printed packet.

The platform covers the two specialties that share evidence, vehicle fraud and auto theft:

• Auto-theft scene documentation captures VIN, damage, forced entry, odometer, and the federal sticker, auto-populates the report from suspect documents, and estimates value.
• VIN verification takes captures across the standard surfaces and flags cross-surface mismatches.
• Plate fraud analyzes temporary tags, paper plates, and dealer-issued plates against state registries and known schemes.
• Dealership audit reconciles lot inventory and books and cites the regulation each violation breaks.
• AI-drafted ROI writes a Report of Investigation in ILETSB format with context-anchored PII redaction (VIN, ORI, badge, and plate kept; DL, SSN, and DOB scrubbed).
• LE-issued forms (VIN certifications, lien releases, title and registration releases) auto-populate from the scan, officer, agency, and suspect data, on agency letterhead.

What makes it different

Every flag references a specific authority, and the output uses articulable language ("exhibits indicators consistent with," not "is stolen"). The printed packet discloses which AI model produced the analysis, which is what admissibility under the Daubert and Frye standards requires. It augments an agency's records management system rather than replacing it, and it runs on a Section 889-clean stack operated by a veteran-owned small business.

Transparency and limits

• DAN-O will not conclude a vehicle is stolen. Only NCIC can. It surfaces indicators and points you to the authority that decides.
• It does not substitute for an officer's judgment, and it does not surface unbacked hunches.
• FraudTrax does not run inside the CJIS perimeter. The architecture assumes any single layer could fail and uses defense in depth: origin gating, tier-based authorization, per-IP and per-account rate limiting, encrypted-at-rest storage, and signature-verified webhooks.

One call, not fifteen. SVT groups every stolen-vehicle record by the manufacturer's law-enforcement contact, so an investigator makes a single activation call instead of fifteen. Coordinated across agencies, fully audited, and AI-searchable.

Why we built it

When a vehicle is confirmed stolen, the fastest path to recovery is activating the manufacturer's connected-car tracking, OnStar and the rest. The catch is that every manufacturer has its own law-enforcement line, and an investigator working a batch of stolen records can burn the critical recovery window just finding the right numbers and calling around. SVT was built to delete that bottleneck so officers spend the window recovering vehicles, not dialing.

The problem it confronts

Recovery odds drop by the hour after a theft, yet the activation step is still manual, fragmented across dozens of manufacturer contacts, and rarely coordinated between the agencies working the same ring. The work that should take one call takes fifteen, and the records that should be shared sit in separate systems. Some manufacturers will not activate at all without a subpoena or a search warrant, which stacks a second delay on top of the first while the officer drafts the legal process by hand.

How it works

• Auto-sorts stolen records to the correct manufacturer law-enforcement contact.
• Ships with a 35-manufacturer LE directory preloaded, so there is no hunting for the right number.
• RAG-based AI search finds a vehicle or record in seconds.
• When a service provider will not move without a court order, DAN-O, a sovereign law-enforcement-specialist engine, drafts the subpoena or search-warrant go-by with current case-law language and your case details, ready for the prosecutor and the judge to review in minutes. Your case information never leaves the sovereign engine, so the roadblock becomes a few minutes of review instead of days of lost recovery time.
• Logs every activation to a tamper-evident, court-defensible audit trail.
• Runs server-backed so departments coordinate activations in one place.

What makes it different

It is built from real field workflows rather than a developer's guess at them, and it runs on DAN-O, the sovereign analysis engine, so case data stays inside the stack. The audit trail is tamper-evident, which matters when an activation becomes part of a case file.

Transparency and limits

SVT coordinates and documents the activation. The manufacturer's own system performs the vehicle tracking and recovery. SVT is access-gated to verified law enforcement. Interview: agency-instance options and any approval or countersign workflow you want stated.

Find the signal. Follow the trace. Hatchet Trace is local-first OSINT monitoring that runs on your own hardware: more than 14 sources swept on a five-minute cadence, case scoring, and a dedicated auto-theft and VIN-fraud module. No vendor cloud, and court-ready chain of custody by design.

Why we built it

The intelligence that breaks a theft or threat case is scattered across marketplaces, forums, and messaging channels that no investigator has time to watch by hand. The OSINT tools that do watch them usually run in a vendor cloud, which means sensitive case data leaves the agency, and they cost more than a small department can justify. Hatchet Trace was built local-first for exactly that reason: the monitoring runs on the agency's own hardware, and the case data never leaves the host.

The problem it confronts

A stolen vehicle or a cloned VIN gets relisted across Craigslist, Facebook Marketplace, Reddit, and Telegram faster than a person can track, and the listing that proves the case can vanish before anyone captures it. Investigators need continuous watch across many sources at once, and they need the evidence preserved in a way that holds up later.

How it works

• Sweeps more than 14 OSINT sources (RSS, Reddit, public Telegram, imageboards, government feeds, and marketplaces) on a five-minute cadence.
• Runs a dedicated auto-theft and VIN-fraud module that watches for stolen vehicles, cloned VINs, and title washing, with VIN validation and NICB alerts.
• Scores per-investigation keywords HIGH, MED, or LOW and correlates hits automatically across sources.
• Captures evidence with SHA-256 hashing and EXIF and GPS data into a tamper-evident audit log modeled on the CJIS Security Policy 5.5.6 audit requirements. That is a design alignment, not a certification.
• Sends real-time HIGH and MED alerts to Telegram and writes on-device case summaries with DAN-O, so nothing leaves the host.
• Adds optional premium deep-dives (Instagram, TikTok, X, YouTube, eBay) with one free run a month and a small per-run fee after that.

What makes it different

It is local-first and self-hosted. There is no vendor cloud, the case data stays on the agency's hardware, and the chain of custody is modeled on CJIS Security Policy expectations. Omega Point holds no CJIS attestation and no CJIS Security Addendum, so authorization for any deployment rests with the agency and its state CJIS Systems Agency. That combination is what makes it usable in environments where a hosted OSINT tool simply is not allowed.

Transparency and limits

Hatchet Trace monitors public and open sources. It is not a backdoor into private accounts. The premium deep-dive runs are metered beyond the monthly free run, and access is gated to verified law enforcement through a request-access onboarding.

Work the case that went cold. Upload the case file and Ice Breaker reads every page, builds a page-cited investigative synopsis, flags the evidence worth re-testing with today's forensics, and surfaces unsolved cases that share the same M.O. The file never leaves your machine.

Why we built it

I was pulled into a cold case that has run through three generations of law enforcement in my own family. It is still open. I built Ice Breaker hoping it helps bring justice, and some measure of peace, to the victim and his family, and to every family still waiting on a case like it.

Cold case files are also the most sensitive data an agency holds: victim identities, juveniles, active leads, and Brady material. The AI that could actually help work a file this size almost always wants to ship it to a cloud, which is a non-starter for this data. Ice Breaker is built so the file physically cannot leave the box. You get the analytical help without giving up custody of the case.

The problem it confronts

A cold case can be 30 to 60 years old. The records arrive in every form: handwritten, typewritten, printed, and sometimes damaged by the flood or fire that hit a records room somewhere along the way. Before anyone can work a lead, someone has to reconstruct what evidence was collected and what was missed, what was tested and what never was, and which of the untested items could be retested with today's capabilities. That last question is often the key piece of the whole puzzle.

Cold cases also rarely get the attention they deserve. Public interest fades, manpower is short, and a steady stream of new crimes keeps pushing the old ones down the priority list. Families feel that silence, and over the years it hardens into a real divide between a community and the agency that serves it. I have heard those concerns firsthand, and I have seen the roadblocks. Ice Breaker exists to clear them.

How it works

• Upload the case file. Drag in the binder, thousands of pages, PDFs, and scans. Ice Breaker indexes every page and OCRs the scanned ones, so every fact it states later points back to an exact source page.
• Analyze. It auto-populates people, dates, addresses, a timeline, and an evidence log, then writes an investigator synopsis: steps taken versus steps needed, probable persons of interest with rationale, witnesses to re-interview, evidence to re-test, and the modern forensics that apply to the old findings. Every claim carries its citation.
• Work the leads. The analysis steps become a tracked task list. The de-identified M.O. is matched against your other cases, and each match shows exactly why it lined up. Export a court-minded report to PDF, DOCX, CSV, Markdown, or JSON when you are ready.

Inside that workflow, an investigator can securely review the case and its photographs, compare and enhance images, and work the puzzles a case can hide in: riddles, codes, and cipher poems. For every piece of evidence, Ice Breaker surfaces what was collected, where it was last stored, and which of today's forensic capabilities it now qualifies for, with page citations and the original documents left completely unchanged. Work that used to take days happens in two to five minutes, so the investigator spends the time following leads instead of rebuilding the file. The goal is plain: move a disposition from COLD to CLOSED.

What makes it different, the sovereignty boundary

Ice Breaker is built so the case file cannot be shipped to a cloud.

• Runs on the investigator's machine. The server binds loopback only, so there is no public endpoint to leak to.
• Encrypted at rest. The case database lives in memory and is written to disk only as an AES-256-GCM vault that your passphrase unlocks. Plaintext never touches the disk.
• DAN-O runs on-box. The analysis happens locally. No case text is sent to any outside service.
• Nationwide matching stays abstract. Only a de-identified M.O. pattern is ever eligible for cross-agency matching. Never names, never addresses, never case numbers.

Transparency and limits

Ice Breaker is investigative-aid software. Its outputs are leads and hypotheses for a sworn investigator to corroborate, not conclusions, and persons of interest are framed as hypotheses to run down. It deploys locally, per investigator or per agency, and is restricted to sworn law enforcement and licensed investigators.

DAN-O Desk puts a law-enforcement-specialist AI in the officer's browser. Ask a question, clean up a report, build a form, or pull a search-warrant go-by. Sign in and work. Nothing to install.

Why we built it

Officers lose hours every shift to paperwork: the report, the supplement, the consent form, the warrant. The general AI tools that could speed that up have two problems for police work. They do not know how a report or a probable-cause narrative is actually built, and they feed what you type into their own training pipelines, which is a non-starter for warrant facts and case data. DAN-O Desk is the answer to both. It is a specialist, and it runs on our own infrastructure, with no third-party AI API, and nothing you write is used to train a model.

The problem it confronts

Writing is where good police work goes to die on time. Getting the structure right, the probable-cause narrative, the nexus, the staleness reminder, takes experience that a new officer does not have yet and a busy one does not have time for. And the moment a tool needs the real case facts to help, the question becomes where those facts go. DAN-O Desk keeps them on the device and still does the heavy lifting.

How it works

Pick a duty mode (Rookie for fast, simple work, or Seasoned Street Cop, the recommended default for reports, warrants, and advice), then use the tools:

• Ask DAN-O for questions, advice, and analysis.
• Grammar and editing cleans up text and lists what changed, without altering the facts.
• Report formats for incident, supplemental, arrest, use-of-force, probable-cause statements, field interviews, crash narratives, and evidence reports.
• Form builder for consent to search, evidence receipts, witness statements, Miranda warning and waiver, tow and inventory, field contact, and chain-of-custody.
• Search-warrant go-bys for residential, vehicle, cell phone, stored communications (provider, Section 2703), GPS and telematics tracking, DUI blood draw, search of a person, and anticipatory warrants, each with the probable-cause structure, nexus and staleness reminders, and an affiant checklist.

You can ground DAN-O in your own documents with an on-box knowledge base. It reads and embeds the files on the machine and cites the source it used. Nothing is uploaded.

What makes it different

It is a law-enforcement-specialist AI, not generic chat, and it runs end to end on Omega Point's own infrastructure. There is no third-party AI API in the path, so warrant and case data never reach an outside vendor and are never used to train a model.

Transparency and limits

Everything DAN-O produces is a draft, not legal advice. It will not invent statutes, case citations, names, dates, or VINs. For any fact it was not given, it inserts a clearly marked placeholder. Every warrant is reviewed by a prosecutor or ASA and authorized by a judge before use, and procedures vary by jurisdiction, so verify against your state law and agency policy.

Centerfuse is the operating system for the whole emergency-operations team, the center where a county's Fire, Police, Sheriff, EMS, EMA, public health, and public works response fuses into one live picture.

Why we built it

Interview: the Marion County EMA origin and what you could not buy off the shelf.

The problem it confronts

Interview: where coordination breaks down during a real event, with agencies on different systems.

How it works

One operating picture across agencies: hazard and GIS layers, real-time feeds, risk briefings, public bulletins, capability assessment, and a grant pipeline, configurable per venue.

What makes it different

It is proven on a live county EOC, runs on infrastructure a county auditor will sign off on, and is one picture across every agency rather than a single department's tool.

Transparency and limits

Interview.

DAN-O is the sovereign law-enforcement-specialist AI beneath every Omega Point product. Built so an agency can finally use AI without its information leaking to a cloud or a third party.

Why we built it

Crime is rampant, and its methods only get more advanced as criminals pick up technology and AI. Law enforcement should have the same edge, but agencies have stayed away from AI for a good reason: the mainstream tools send their information to the cloud and to third parties, where it can leak. So I built a law-enforcement-specialist AI that does not do that. I named it DAN-O, after the Hawaii Five-0 detective, and the rule is simple. It does not leak or share an agency's information.

The other half of the problem is what we ask of officers. Thanks to television, the public expects a cop to handle every situation imaginable and close the case in seconds. The reality is that officers are buried in paperwork and forms, in everything that is not protecting their neighborhood or stopping the next victim. Used responsibly, AI is a priceless tool. It can meet the documentation requirements far more efficiently so the human in the loop can deter crime, spend unhurried time on calls instead of rushing the next one, articulate incidents more clearly, and keep pace with the people they are up against. DAN-O is the engine that makes that possible.

The problem it confronts

Two gaps, at the same time. Officers lose their shift to documentation that pulls them off the street, and the AI that could give that time back has been off limits because it could not be trusted with sensitive data. DAN-O closes both: it does the documentation work, and it does it without the information ever leaving the agency's control.

How it works

DAN-O is governed retrieval over a curated corpus plus law-enforcement-specialist reasoning. It drafts reports, forms, and warrant go-bys, coaches probable-cause and nexus, cites the authority behind a finding, refuses to fabricate statutes or citations, and uses clearly marked placeholders for any fact it was not given. A human stays in the loop on every output, and it is exposed to partners as an API so an integrator can build on the same core the products run on.

What makes it different

It is sovereign, U.S. built, and specialized for police work rather than adapted from a general consumer model. The same engine powers every Omega Point product, so each one inherits a core that has already been tested in the field, and none of them ship an agency's data to an outside service.

Transparency and limits

DAN-O produces drafts and analytical aids, not conclusions and not legal advice. It does not replace an officer's judgment, and every output is meant to be reviewed by the human in the loop and, for legal process, by a prosecutor and a judge.

ODIN

ODIN is a separate, proprietary engine, invented by Cody A. Rose and held by Omega Point Solutions. Its capabilities are deliberately not detailed here. It is documented and dated in our records, with formal protection in progress.

Seeing is no longer believing. Omega Lens is a sovereign recognition engine that judges whether an image, a voice, or a video was generated or manipulated, and can restore the missing context around a clip that was cut to mislead. It runs on your own hardware, using open-weight models, so no media ever ships to a third-party detector.

Why we built it

Generated media is now cheap enough to weaponize at scale: cloned voices for fraud calls, face-swapped video for extortion and disinformation, synthetic images to prop up a clone site. The tools that spot these are usually cloud services, which means handing your most sensitive media to a third party. Omega Lens was built so the detection can run where the compute already lives, on the operator's own box, and so the same trained-model brain can serve every Omega Point product without any of them shipping evidence off-site.

The problem it confronts

Two problems, really. First, trained neural detectors do not belong in a lightweight edge worker, they need real model runtime and weights, so the heavy judgment has to live somewhere with compute. Second, a clip can lie by omission: a real ten-second cut, pulled from a longer source and stripped of what came before and after, can mislead as badly as an outright fake. Omega Lens addresses both, the trained-model verdict and the missing-context recovery.

How it works

• Image, audio, and video recognition. Fetches the media under an SSRF guard, then routes it to the right detector: AI-image and face-manipulation classifiers, voice-spoof and clone classifiers, and frame-sampled aggregation for video. Devices are auto-detected, models are lazy-loaded.
• Context restoration. For a suspicious clip it runs edit forensics (hard cuts, audio splices, very short duration), a verbatim timestamped transcript, transparent rule-based technique and arousal flags, and source recovery by perceptual fingerprint against a sovereign corpus you index, with clearly-flagged external reverse-search only as a fallback.
• Grounded, viewpoint-neutral output. The factual account is extractive: the excerpt's own words, the edit and technique findings, and, when the source is recovered, the full-context window of what came before and after. If the source is not found, it says so plainly. It flags rhetorical technique, never viewpoint, party, person, or the truth of a claim.
• Serves the suite. Omega Guard's orchestrator keeps the cheap edge signals (Content Credentials metadata, perceptual hashing) and delegates the trained-model verdict to Omega Lens.

What makes it different

It is sovereign and honest. The models run on your hardware with open weights, so nothing leaves the box, and the design is explicit that a verdict is corroborating evidence rather than proof. The context-restoration pass is deliberately nonpartisan: it exposes how a clip was cut and what surrounded it, without taking a side on the content.

Transparency and limits

State-of-the-art detectors are beaten by novel generators, so low-confidence and uncertain results must be treated as exactly that. Omega Lens produces scores and evidence for a human to weigh, and no verdict should drive an alert, a takedown, or an export without that human review.