Ship AI in regulated environments — without the compliance rewrites.
I help financial firms deploy AI they can actually trust — accurate, grounded in your data, and governed well enough to pass a risk review. I don't just advise. I build the system that proves it works.
Most enterprise AI never makes it past the demo.
It hallucinates in front of clients, can't explain itself to risk and compliance, or stalls in the gap between a clever prototype and something you can actually run in production. In financial services, the bar isn't "impressive demo" — it's accurate, auditable, and approved. Mathneo AI closes that gap.
A clear path from first conversation to ongoing partner.
Each stage is time-boxed with concrete deliverables. Start with a free call and stop at any point — no long-term contract required.
Typical engagement: 1–16 weeks · fixed scope · artifacts delivered into your environment.
- Free
Discovery Call
30 minutes · freeA no-pressure conversation to understand your goal, constraints, and where AI actually fits — or doesn't.
- Honest read on feasibility and risk
- Suggested next step (or none, if AI isn't the answer)
AI Readiness Sprint
2–3 weeks · fixed scopeA focused assessment of your data, use cases, and risk posture — separating what's real from what's hype.
- Prioritized use-case shortlist with feasibility scoring
- Costed 90-day roadmap with build-vs-buy calls
- Risk register mapped to your compliance framework
AI Evaluation & Guardrails
2–4 weeks · fixed scopeEvaluation harnesses and controls your risk and compliance team needs to approve — or defend — a launch.
- Evaluation harness with accuracy, safety, and hallucination metrics
- PII, bias, and prompt-injection test suite
- Audit logging + governance one-pager for risk review
RAG Pilot in 6 Weeks
4–8 weeks · production-readyEnd-to-end build on AWS Bedrock or SageMaker — retrieval grounded in your data, with citations and guardrails.
- Working assistant that cites its sources on every answer
- Guardrails, human-review workflow, and monitoring dashboard
- Deployment in your AWS tenant — your data never leaves
Ongoing AI Partner
Monthly retainer · optionalA senior partner on call — for reviews, evaluations, and quiet iteration once your system is live.
- Monthly evaluation runs + governance updates
- Architecture and vendor reviews on request
- Direct access to Hassan — no account layer
Senior, unbiased, and accountable end-to-end.
Mathneo AI is an AI engineering practice specializing in compliance-safe agentic AI for financial services. Founder Hassan brings 15+ years of ML and data engineering at scale — most recently building an AI-enabled financial coaching platform inside a Fortune 500 firm, including agentic orchestration, PII-masking architecture, and the evaluation systems that carried it through internal risk review. Our fixed-scope readiness audit packages that production experience into an engagement your team can act on in weeks, not quarters.
Advise and build
One person owns strategy and code — no handoff, no lost context between the deck and the deploy.
Trust is the whole point
Grounded, factual, and auditable — engineered to survive a risk review, not just a live demo.
Deep financial domain
Speaks insurance, banking, and wealth — and the realities of regulation, controls, and audit.
Independent & vendor-neutral
No big-firm markup and no platform to push. The right stack for your problem, not mine.
From hours to minutes, per claim.
At a workers'-comp insurer, adjusters were spending hours on every claim, reading and summarizing dense medical records by hand.
Built an LLM summarization pipeline grounded in the source documents, with accuracy checks so adjusters could trust — and verify — the output.
Summary time dropped from hours to minutes per claim, with adjusters fully in control of the final call.
Client kept anonymous under NDA.
A low-risk path from idea to production.
Assess
Find the highest-value, lowest-risk use case and the gaps in your way — you leave with a clear, costed plan.
Prototype
A working proof-of-concept on your own data, so you see the result and the ROI before committing to a build.
Build & operate
Ship it production-grade — grounded, evaluated, governed — then keep improving it against real usage.
Senior owner-operator. You work directly with the person building it.
Mathneo AIHassan — founder of Mathneo AI. Ex-Amazon (AWS), with 15+ years building ML and data systems at scale, including at Thomson Reuters. MS in Data Science.
I specialize in trustworthy, factual AI — building GenAI systems (RAG assistants, LLM applications) on AWS Bedrock and SageMaker that are accurate, grounded, governed, and production-ready. My clients are insurers, banks, and wealth firms. I'm currently building AI-enabled data architecture and RAG capabilities for a financial-services firm.
Most AI consultants stop at the recommendation. I keep going — deciding what's worth doing, then building and shipping the system that proves it. Grounded in your data, evaluated for accuracy, and governed so it survives a risk review.
Common questions before we talk.
Are you a solo consultant or an agency?+
Solo and independent — that's the point. You get a senior practitioner who both scopes the work and writes the code, with no handoff to junior staff and no big-firm overhead in the invoice. Engagements are scoped in short, well-defined phases, with working artifacts — code, evaluations, and documentation — checked into your environment at every milestone. The work never lives only in one person's head.
How do engagements usually start?+
With a free 30-minute call to pressure-test whether the use case is worth doing. If it is, most clients start with a 90-minute Trustworthy-AI Readiness Review ($1.5K) — a working session plus a 2-page roadmap — before committing to a larger build.
How much do full engagements cost?+
It depends on scope, data readiness, and how much of the build sits inside your environment. I'll give you a fixed-fee or capped proposal after the readiness review — never an open-ended clock.
Do you work inside our cloud and security perimeter?+
Yes. Builds run in your AWS account (Bedrock / SageMaker) against your data, under your IAM, logging, and DLP controls. Nothing leaves your environment without your say-so.
Can you work alongside our existing data or engineering team?+
Often the best setup. I lead the AI-specific work — retrieval, evaluation, guardrails, governance — while your team owns the surrounding platform. I document as I go so nothing walks out the door with me.
What if we're not sure AI is even the right answer?+
Good — that's the healthiest place to start. Part of the readiness review is telling you honestly when a simpler tool, a process fix, or 'not yet' is the better call.
Let's see if AI is worth doing for you — and prove it.
Book a 30-minute call, or send a short note about the use case you're weighing — I'll reply with a candid take on whether it's worth doing and how I'd approach it.