Intake · PI ENGINE
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OptiVis
AI engineering · Sugar Land, Texas
We build production AI that proves its own work.
We build and operate production AI for complex commerce catalogs and the document workflows of legal and professional firms.
Every engagement starts with a fixed-fee diagnostic: $7,500, ten business days, one workflow.
Selected work
The work. Then the evidence.
At an auto-parts retailer whose Shopify store we build and run, ChatGPT referrals converted at 12.6% in Q3 2026 (19 orders from 151 sessions), against 1.9% for Google search (76 orders from 3,988 sessions). ChatGPT was the store’s second-largest referral source by sales, behind Google. Small numbers, measured in Shopify attribution; we re-measure every quarter.
Auto-parts retailer · Shopify online store
Commerce practice →
3.1x
Online Store sales, Q3 2026 vs Q3 2025
Q3 2025
$8,137
Q3 2026
$25,123
Same quarter, year over year. Both bars start at zero on the same scale.
12.6%
ChatGPT referral conversion
19 orders from 151 sessions
1.9%
Google search conversion
76 orders from 3,988 sessions
#2
ChatGPT’s rank among the store’s referral sources by sales, behind Google
Ranked by attributed sales, excluding direct traffic and the store’s own domain
Window: July 1 to September 30, 2026. Source: Shopify attribution for the store. This is a client’s store, not ours.
Method and source
Client and channel: an auto-parts retailer whose Shopify store we build and run. The sales comparison uses the Shopify Online Store channel.
Sales: July 1 to September 30, 2025, $8,137.31. July 1 to September 30, 2026, $25,123.32. $25,123.32 ÷ $8,137.31 = 3.087, shown as 3.1x.
Conversion, July 1 to September 30, 2026: 19 ÷ 151 = 12.58% for ChatGPT and 76 ÷ 3,988 = 1.91% for Google search, rounded to one decimal. Shopify's sessions report counts sessions that completed checkout; the separate referral sales report records matching order counts of 19 and 76.
Ranking: named referral sources ranked by attributed sales, excluding direct traffic and the store's own domain.
Paid clicks: the 144 paid ChatGPT ad clicks in September 2026 are not inside the 151 referral sessions.
Source: Shopify reports extracted October 4, 2026. Small sample. Attribution is not evidence of causation or profit.
How to read it
Attribution shows where orders came from. It does not prove profit, and it does not prove that our work caused them. Early AI-referred shoppers tend to arrive ready to buy, so we project conservatively and publish the next quarter whatever it shows. On a build, the diagnostic sizes the business case for your numbers before you commit.
Read the commerce practice and the business case →
Products and research from OptiVis Labs
Conversations the systems are built to handle
Intake, deadlines, adjuster offers, deposition prep, case status and parts fitment, from PI ENGINE, LITIGATOR and our commerce systems.
Two practices. One standard of evidence.
Practice 01
AI commerce engineering
For
retailers and catalog operators with product data that is hard to get right: fitment, kits, variants, feeds.
What we build
catalog and fitment engineering, product data that answer engines can read, store monitoring, and AI systems that run on the catalog.
What it is not
a theme, a store setup or an ad campaign. Those are OptiVis Marketing.
Proof
the auto-parts result above.
Commerce practice →Practice 02
Private AI for legal and professional firms
For
firms whose documents and client information cannot go into a public AI tool.
What we build
intake response, document review with verified quotes, and litigation research, inside documented data boundaries.
What it is not
legal marketing, a chatbot widget, or a cloud subscription with your files in it.
Proof
a document system that checks every quote character for character against the source and refuses any quote that fails; designed with a practicing Texas trial attorney who is trial counsel at OptiVis.
Legal practice →Diagnostic. Build. Operate.
Diagnostic
$7,500 fixed
Credited in full against a build signed within 60 days.
Ten business days on one workflow. You get a workflow and data map, baseline numbers, a data-boundary map, a build-or-buy comparison, acceptance tests, a fixed-price build quote and a written go or no-go. You own all of it.
Next step after: written go or no-go
Fixed-price build
From $25,000
Hardware, model usage and licenses itemized separately.
Phase 1 puts one workflow into production on your data, with an exit point. Final payment is due on your written acceptance against the acceptance tests. You own the code, prompts, evaluation sets and data.
Next step after: written acceptance
Operate
From $2,500 a month
Optional. Only for systems we built.
Monitoring, quarterly model re-evaluation and a monthly operating report, within written limits.
How it works, with weeks and acceptance gates →
Need a website, SEO or ads rather than an AI system? That work is handled by our marketing team, OptiVis Marketing.
We operate the kind of systems we build for you.
Our own work runs on GPU compute we operate, scheduled by a job router we wrote. Before it sends work to a machine, it proves the machine with a real computation. It splits jobs into checkpointed units, retries a failed unit on a different machine, and keeps a ledger you could reproduce.
Private inference runs on open-weight models on the same compute. Our monitoring pushes alerts, and an off-site probe on a different internet provider watches from outside the office.
When we pick a model for a task, we pick it on a blind evaluation harness across Claude, GPT, Grok, Kimi and open-weight models, the way an engineer picks a part.
We run our own trucking operation on the same kind of systems we build: loads checked against the truck’s location and the driver’s remaining hours, pay compared with lane history, fuel records reconciled. A dispatcher makes the booking call.
How OptiVis Logistics runs
Honest ceiling
No single card in our compute has more than 16 GB of memory. Models that need more run on hardware you own or in an isolated cloud environment, and the diagnostic says which.
Client data is processed only within the deployment boundaries agreed in writing. Any external provider, data transfer, and retention policy is documented before use.
Models and platforms we build with
We choose the stack around your workflow, data boundaries, and acceptance tests.
Frontier models
- ClaudeAnthropic
- GPT and CodexOpenAI
- GrokSpaceXAI
- KimiMoonshot AI
- PerplexityWeb research
Decision and evaluation
- JevTypeSafe AI
- LayaConvai, open source
- Blind evaluation harnessOptiVis
Open-weight and custom models we host
- Qwen-ImageAlibaba Qwen
- DeepSeek-OCRDeepSeek
- PaddleOCR-VLBaidu PaddlePaddle
- dots.ocrrednote hilab
- WhisperOpenAI
- BGE-M3BAAI
- SAMMeta
- Qwen3Alibaba Qwen
- Llama 3.2Meta
- PhiMicrosoft
Pulled from Hugging Face and run on GPU compute we operate. Custom models built on open weights.
Compute
- CUDA GPUsNVIDIA
- GPU job routerOptiVis
- RunPodBurst GPUs
Media and voice
- HiggsfieldImage and video
- ElevenLabsVoice
- FireflyAdobe
- Creative CloudAdobe
Platforms we ship on
- ShopifyCommerce
- VercelHosting
- CloudflareEdge and DNS
- FramerSites
- HubSpotCRM
- ZapierAutomation
- Google WorkspaceGoogle
Private AI with documented data boundaries.
“Private” is a claim a procurement reviewer should test, so we write it down. For every system we build, a data-boundary document states where each component runs, which model calls leave your environment and to which provider, and how OCR, embeddings, logs, backups and support access are handled, with retention and deletion. You get the first version in the diagnostic, before anything is built.
Data-boundary document structure
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.
The people on this page are the people who build your system.
Abdullah Siddiqui
Founder, CEO & Head Systems Architect
Designs the architecture and the AI systems.
Alexis George Cownan, Esq.
Trial Counsel
Practicing Texas trial attorney. Co-developed LITIGATOR and decides what in a legal workflow gets automated and what stays with the attorney.
Hasan Abdul Kadir
Director of Logistics and Technologies
Oversees deployment, testing and client onboarding.
About OptiVis →
Who this is for, and who it is not for.
For you if
• A wrong answer or a lost lead in one workflow costs you real money.
• Your data cannot go into a public AI tool as it stands.
• You want a system in production, measured against a number you set, not a strategy document.
Not for you if
• You need a website, ads or local marketing. That is OptiVis Marketing, at optivismarketing.com, with its own deliverables and prices.
• You want a chatbot widget, general IT support or an AI strategy deck.
• Your budget is under the $7,500 diagnostic.
What we refuse
• Strategy decks in place of systems.
• Discovery that exists to bill discovery.
• An ROI promise before we have seen the workflow.
Production builds are fixed-price and start at $25,000.
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.