The firm
About OptiVis
We build and operate production AI for complex commerce catalogs and the document workflows of legal and professional firms.
Why we exist.
Many businesses run on tools assembled in pieces that were never built to work together. We build the system that connects them, put it into production on your data, and measure it against a number you set.
Every engagement starts with a fixed-fee diagnostic: $7,500, ten business days, one workflow.
Proof.
Our published evidence, with its denominators, sources and limits, is in the retailer exhibit on our homepage.
One retailer
Commerce is an application of the work, not a practice.
An auto-parts retailer whose Shopify store we build and run. One measured window, both denominators, and the limit of what it shows.
Shopify session report, Q3 2026: ChatGPT, 19 orders from 151 sessions, 12.58%; Google search, 76 orders from 3,988 sessions, 1.91%.
The limit. Attribution shows where orders came from. It does not prove profit, and it does not prove that our work caused them. Small sample, one store, one quarter.
- Status
- We build and run the store.
- Boundary
- The client’s store and the client’s Shopify data.
- Acceptance
- Re-measured from Shopify attribution each quarter, whatever it shows.
The evaluator
We taught a smaller model to make the same calls as the evaluator.
The evaluator is Jev, from TypeSafe AI. The smaller model is ModernBERT-large, distilled from 68,828 Jev-labeled training examples, so the same calls run on compute we operate.
It is measured as teacher agreement, not accuracy, on two cohorts, with zero-shot Laya as the baseline.
In-KB n = 4,387
Unseen KB n = 1,301
The limit. Rare flags are not claimed.
- Status
- Measured. The figures are published on OptiVis AI Labs.
- Boundary
- Trained and run on GPU compute we operate.
- Acceptance
- Teacher agreement on two cohorts, against a zero-shot baseline.
Engineering standard
One standard under every system.
Evaluation
A system is measured against a baseline before we call it done, the way the distillation above was measured against zero-shot Laya.
Evidence-backed retrieval
Answers point to the passages they came from. A quote that does not match its source is refused, not shown.
Written acceptance
Acceptance tests are written down before the build, and the build is accepted against them.
Engagement
How an engagement is priced.
Diagnostic
$7,500
Ten business days, one workflow. Credited in full against a build signed within 60 days.
Production build
From $25,000
Fixed price, accepted against tests written before the build.
Operate
From $2,500 a month
Bounded and optional.
What makes us different
Private AI with documented data boundaries.
Client data is processed only within the deployment boundaries agreed in writing. Any external provider, data transfer, and retention policy is documented before use.
You get the first version in the diagnostic, before anything is built.
- Component placement
- Where each component runs
- Model calls
- Which provider receives which data
- OCR
- Where processing occurs
- Embeddings
- Where vectors are stored
- Logs
- What is retained
- Backups
- Where copies reside
- Support access
- Who can access what
- Retention and deletion
- Duration and removal procedure
These are the fields each document answers, not a record of a particular deployment.
- Intake
- PI ENGINE handles intake. It is in pilot with one firm.
- Litigation
- LITIGATOR supports litigation document intelligence with source-linked answers and attorney review.
- Counsel
- Both were designed with a practicing Texas trial attorney who is trial counsel at OptiVis.
People
The people behind OptiVis.
Founder, CEO & Head Systems Architect
Abdullah Siddiqui
Abdullah founded OptiVis in late 2024 after years of working in the backend infrastructure and operations of law firms. He designs the architecture and the AI systems, from CRM and case-management integrations to local models on edge hardware.
Director of Logistics and Technologies
Hasan Abdul Kadir
Hasan brings years of experience in technology infrastructure and operational logistics to OptiVis. He oversees deployment, integration testing and training for each client team.
Trial Counsel
Alexis Cownan
Alexis Cownan is a practicing trial attorney based in Houston and trial counsel at OptiVis. He co-developed LITIGATOR, the litigation document intelligence product published by OptiVis AI Labs, and decides what in a legal workflow gets automated and what stays with the attorney.
What we don’t do.
We don’t bill hourly. Diagnostics and builds are fixed-price, and Operate is an optional monthly service.
The systems we build are custom to your workflow.
We don’t take cases. We’re a technology firm. Your clients are yours.
We don’t push cloud-only AI on you. Where each component runs is agreed in writing before anything is built.
Our properties
OptiVis LLC, founded in 2024 by Abdullah Siddiqui in Sugar Land, Texas, runs four properties: OptiVis (AI engineering), OptiVis Marketing (small-business marketing), OptiVis AI Labs (products and research) and OptiVis Logistics (applied AI in logistics).
Questions.
Who founded OptiVis, and what does it do?
OptiVis is the AI engineering firm operated by OptiVis LLC in Sugar Land, Texas, and founded by Abdullah Siddiqui. Commerce is an application of that work: complex catalogs, fitment, and AI shopping channels. Its work for legal and professional firms includes private legal AI and intake.
How do OptiVis, OptiVis Marketing, OptiVis AI Labs and OptiVis Logistics relate?
OptiVis LLC operates related businesses with distinct roles. OptiVis provides AI engineering; OptiVis Marketing provides websites, SEO, advertising and content; OptiVis AI Labs publishes products and research; and OptiVis Logistics applies AI to trucking operations. A marketing service, a product pilot and a custom engineering build have different scopes and should be evaluated on their own terms.
Start with the diagnostic.
Send a short project brief. If the workflow is a fit, we book a 20-minute call. If it is not, we say so.