AI Engineering Studio
Most AI pilots are built where a mistake is harmless. We work where it isn't — clinical records, legal documents, financial operations, internal knowledge that people make decisions on.
XtroAI designs and ships retrieval, agent and document systems that are measured before they go live, and can be audited after.
The model is rarely the problem. Projects die on retrieval quality, evaluation, latency and cost — the unglamorous engineering that decides whether anyone trusts the output.
Six systems built across regulated and knowledge-heavy operations.
| № | Engagement | Sector | System built | State |
|---|---|---|---|---|
| 01 | Kresus | Web3 | Conversational trading agent — multi-chain tool execution with confirmation before any on-chain action | Live |
| 02 | Vyera AI | Competitor intelligence | Multi-agent research workflow turning fragmented signals into source-linked briefs | Live |
| 03 | Auxee | Enterprise knowledge | Hybrid retrieval over scattered internal sources, permission-aware, citation-enforced | Live |
| 04 | Chex.ai | Legal technology | Document intelligence — clause-level extraction and review across long, high-stakes contracts | Live |
| 05 | CareVision | Healthcare | Vision and language pipeline turning unstructured records and imagery into reviewable structured output | Live |
| 06 | HighTribe | Travel | Itinerary intelligence and workflow automation wired into the live product | Live |
Detailed write-ups available under NDA on request.
Retrieve, verify, call the tools it's allowed to call, and hand the edge cases to a named person when the evidence isn't strong enough. Every step lands in the audit log.
Task arrives with its document set and the policy scope it must work inside.
Hybrid retrieval across the connected policy, precedent and record stores.
Every draft finding is checked back against the passage it came from.
Low-confidence items route to a named reviewer — never a silent override.
Outcome written with citations and a full audit trail for compliance.
[00:00] task received — contract #LC-4471
[00:04] retrieve · policy + precedent · 6 sources
[00:19] extract · 34 clauses · 4 flagged
[00:33] verify · each flag against its source passage
[00:41] gate · 2 low-confidence → named reviewer
[04:12] ✓ resolved — citations + audit trail written
Illustrative walkthrough of a contract-review agent. The log and these figures are scripted to show the shape of a production run — they are not telemetry from a client system.
Fixed scope, fixed price, a working artifact at the end of each one. You can stop after any of them and still own something useful.
We tell you which of your workflows are worth automating — and, more usefully, which ones aren't.
Your assistant returns the wrong document. The fix is almost never the prompt — it's the retrieval underneath it.
The full system — agents, retrieval, vision or document processing — built into your stack and shipped to real users.
Someone watching the system after launch — quality drift, cost, and the model landscape changing underneath you.
You have three real options for this work. Here is where each one actually costs you — including where we're the wrong answer.
| Hiring in-house | A large consultancy | XtroAI | |
|---|---|---|---|
| Time to a working system | 3–6 months to hire, then ramp-up | Weeks of scoping before anyone builds | Working slice on your data in 2–4 weeks |
| Who writes the code | Whoever you manage to hire | Partners sell it, juniors build it | The engineer who scoped it |
| What you commit to | A salary, permanently | One large multi-month contract | One fixed-price phase at a time |
| After it launches | Your team owns it — if they stay | Retainer, or they leave | Documented handover; support optional |
| If it turns out to be the wrong idea | You've already hired for it | You've already paid for it | The audit says so in week one |
| Where this fails you | Right call if AI is core to your product forever | Right call if you need 40 people and a procurement process | Wrong call if you want a body to manage indefinitely |
Not every project needs AI. The same team designs, builds and maintains the software and digital presence around it too — in-house, not subcontracted out.
The same four phases on every build. Nothing moves to the next one until the previous one has produced something you can look at.
We write down the number the system has to hit before we build it — accuracy on a real question set, time saved per case, cost per request. If we can't agree on that number, the project isn't ready.
Week 1One complete path through the real system on your real data — not a mock, not a demo dataset. This is where retrieval quality, model choice and cost get settled while they're still cheap to change.
Weeks 2–4Full implementation, the eval suite wired into every change, tracing so you can see why the system did what it did, and the integration work into the tools your team already uses.
Weeks 5–10Ship to production with monitoring and cost controls, then documentation, runbooks and a working handover session. Ongoing support is an option — never a dependency we design in.
OngoingEvery phase is fixed-price and independently useful. You can stop after any one of them and keep the architecture, the evaluation set and the code. We don't ask anyone to sign for a six-month build before they've seen us work.
Founder · Senior AI/ML Engineer
Faisal has been building production machine learning since before the current LLM wave — six-plus years across healthcare, legal technology, enterprise knowledge, travel and fintech. He leads architecture and delivery on every engagement: the person who scopes the work is the person who writes the code.
No layered team you never meet, no junior handoff after the pitch. The engineer on the call is the one building it.
If we can't demonstrate whether the system is working, it isn't ready to be in front of your users.
Your repositories, your cloud, your keys, your evaluation sets. No lock-in to us and none to a single model vendor.
A working increment and a written update every week. You never have to ask how it's going.
Experience in this field isn't counted in years — the field isn't old enough for that to mean much. It's counted in how many of the specifics below you've already been caught out by. If they're unfamiliar to whoever else is quoting you, that's the gap.
We don't hold SOC 2 or ISO 27001 yet — plenty of vendors claim compliance without it changing much day to day. Here's what we actually do instead, and we'll say plainly when that changes.
Data in transit is encrypted end to end. Data at rest uses your cloud provider's standard key management — we don't run separate, unencrypted storage for client data outside your own environment.
Access to your systems is scoped to the engagement and revoked at handover, not left open indefinitely. Credentials live in your own secrets manager wherever that's possible, not ours.
Every gate decision, tool call and escalation is logged — the same trail described in Under the hood, not a separate system bolted on for compliance after the fact.
Retention windows are agreed with you per engagement. You control deletion; we don't keep a copy of your data once an engagement ends unless you've asked us to.
Self-hosted and region-pinned model serving are available where regulation requires it, so your data doesn't have to leave your own cloud account to use frontier or open-weight models.
We don't train shared or third-party models on your data. Model choice and routing are documented per engagement, so you're not dependent on one vendor's roadmap or pricing.
None of this replaces your own security review. Ask about anything on this list before you sign — we'll answer directly, including where we fall short of what you need.
The ones that come up on nearly every first call.
With a scoping conversation, then usually a Readiness Audit. It's deliberately small so you can judge the quality of the thinking before committing to a build — and it ends with a written recommendation you keep either way.
A working slice on your own data lands two to four weeks in. Full production delivery is typically six to ten weeks after that, depending on how much integration and review your environment requires.
It stays in your environment. We build inside your cloud accounts and repositories using your provider keys, and we can work entirely with self-hosted or region-pinned models where regulation requires it.
Whichever fits the task, the budget and the compliance constraints — frontier APIs, smaller hosted models, or open-weight models you run yourself. Systems are built so the model can be swapped without rewriting the application around it, because it will need swapping.
That's the intended ending. Every engagement closes with documentation, the evaluation suite, deployment runbooks and a live handover session. If you keep us on afterwards, it should be because you want to.
That's a common starting point and usually a Retrieval Rescue. We diagnose where the quality is actually being lost — retrieval, chunking, ranking or the prompt — and give you before-and-after numbers rather than an opinion.
Describe the workflow and where it goes wrong today. You'll get a straight answer on whether AI is the right tool for it — including when it isn't — and what building it would actually take.
Replies usually within one working day.
Bring a single process that's slow or error-prone. We'll sketch what an AI system for it would look like, what it would cost to run, and whether it's worth building at all. You keep the sketch either way.