· Independent AI Consultant & Fractional CTO
UK · US · Europe
I architect
AI systems that survive
production.
Models improve every month; systems still fail every day. The difference is architecture. I'm Nic Chin, AI consultant and architect.
13 production systems94% retrieval hit-rate0/297 hallucinated citations
call at no charge · paid, fixed-price work from £600
See production systems (opens in a new tab)
- 13 production AI systems shipped
- 94% retrieval hit-rate, six benchmark suites
- legal · fintech · pharma · SaaS
· engagements
What you can buy
Each one is fixed in scope and price before work starts. No hourly billing.
| If this is you | Engagement | What you get | Takes | Price |
|---|---|---|---|---|
| Your app works. Is it safe to launch? | AI-Built App Review (opens in a new tab) | Data access, secrets, auth, payments and any AI feature checked by an architect, with a fix order and a walkthrough. | 5 working days | From £1,000 / $1,300 |
| Users see each other’s data, or results differ between users. | Supabase Architecture Review (opens in a new tab) | RLS policies, storage rules, auth flows and multi-user behaviour reviewed, with a fix order. | Agreed before work starts | From £1,000 / $1,300 |
| An enterprise security review, a pilot or a scale-up is coming. | AI Security & Architecture Review (opens in a new tab) | Six check areas, every finding with its evidence, a fix order and a walkthrough. An architect’s review, not a pentest. | 1-4 weeks | From £2,000 |
| One hard decision: build or buy, which architecture, is this design safe? | Architecture Decision Day (opens in a new tab) | A pre-read, a working day on your system, and a written decision record within 48 hours. Credited in full against a security review. | 1 day | £600 |
| Is this worth building, and what will it cost? | Two-Week Discovery Sprint (opens in a new tab) | A working prototype on your real data, an architecture document, an evaluation set and a fixed build estimate. | 2 weeks | From £2,450 |
| Nobody senior owns the AI roadmap. | Fractional AI CTO (opens in a new tab) | Architecture decisions, code review and accountability for what ships, usually one or two days a week, up to three around a launch. | Monthly | From £1,000 / $1,350 a month (half a day a week) |
Your app works. Is it safe to launch?
AI-Built App Review (opens in a new tab)Data access, secrets, auth, payments and any AI feature checked by an architect, with a fix order and a walkthrough.
5 working daysFrom £1,000 / $1,300
Users see each other’s data, or results differ between users.
Supabase Architecture Review (opens in a new tab)RLS policies, storage rules, auth flows and multi-user behaviour reviewed, with a fix order.
Agreed before work startsFrom £1,000 / $1,300
An enterprise security review, a pilot or a scale-up is coming.
AI Security & Architecture Review (opens in a new tab)Six check areas, every finding with its evidence, a fix order and a walkthrough. An architect’s review, not a pentest.
1-4 weeksFrom £2,000
One hard decision: build or buy, which architecture, is this design safe?
Architecture Decision Day (opens in a new tab)A pre-read, a working day on your system, and a written decision record within 48 hours. Credited in full against a security review.
1 day£600
Is this worth building, and what will it cost?
Two-Week Discovery Sprint (opens in a new tab)A working prototype on your real data, an architecture document, an evaluation set and a fixed build estimate.
2 weeksFrom £2,450
Nobody senior owns the AI roadmap.
Fractional AI CTO (opens in a new tab)Architecture decisions, code review and accountability for what ships, usually one or two days a week, up to three around a launch.
MonthlyFrom £1,000 / $1,350 a month (half a day a week)
Not sure which fits? A 45-minute call settles it.
Bring the system and the decision in front of you. You leave knowing which engagement fits, or with a plain answer that you need none of them. The price is fixed in writing before any work starts.
call at no charge · paid, fixed-price work from £600
· the failure pattern
Why AI Doesn't Reach Production
The same failure patterns appear in almost every stalled AI project - and each one is an architecture decision, not a model limitation.
Nic Chin is an independent AI consultant and fractional CTO who designs and ships AI systems for production - multi-agent automation, retrieval-augmented generation with verified citations, and human-approved workflow automation - for companies in the UK, US and Europe. Thirteen systems are live in production, and the accuracy claims are auditable on a published benchmark run: 94% retrieval hit-rate and 0 hallucinated citations across 297 cases.
Building around a model instead of designing a system
The demo works, then the first model update breaks everything downstream.
Systems outlast models
Design for the model to be swapped, because it will be.
Treating accuracy as a model problem
Teams tune prompts for weeks when the retrieval pipeline is what is failing.
Accuracy is a system property
A 94% retrieval hit-rate is architecture, not prompt engineering.
Confusing a working demo with a production system
Real users, compliance reviews, and failure modes arrive after the applause.
Production means production
If it can't handle real users, compliance, and failure, it's a prototype.
Optimising before measuring
Without an evaluation framework, every change is a guess.
Evidence over opinion
Every accuracy figure on this site comes from a published benchmark run you can audit.
Writing code before understanding the problem
The most expensive AI mistake is building the wrong system correctly.
Architecture before code
The design decisions made before week one determine whether AI ships.
· evidence
Selected Production Systems
13 systems in production across five capability areas. Every claim below is verifiable - two are live products you can use today.
Production AI Platforms
Turning prototypes into production-grade systems.
document intelligence / multi-tenant SaaS
SureCiteAI
Document AI that answers with sources - and survives real multi-tenant production traffic
147ms responses and a 94% retrieval hit-rate across six benchmark suites, with zero cross-tenant leakage - live at sureciteai.com
- CoachIQ - multi-modal computer vision at 95% analysis accuracy with API-failover architecture
- SculptAI - multi-agent system that raised $350K at $4.8M FDV
70%+
manual review time eliminated
confidential legal engagement - details in the portfolio
Enterprise Knowledge Systems
Helping organisations retrieve, reason and answer with confidence.
legal / investment funds
LPA Analyzer
150-200 page fund agreements taking lawyers 4-6 hours each to review
70%+ review time eliminated with clause-level RAG analysis inside Microsoft Word
- Pharma QA platform - enterprise pilot approval, 365-day append-only audit trail
- SureCiteAI hybrid search - dense + sparse retrieval at a 94% retrieval hit-rate across six benchmark suites
Intelligent Automation
Replacing manual workflows with AI orchestration.
creative business / marketing
AI Marketing Intelligence Platform
15-20 hours a week of manual content, lead discovery, and email work
5-7 hours back every week, with 98% brand voice consistency across 5 orchestrated tools
- Enterprise utility platform - IoT billing automation for the German property market
- n8n automation ecosystem - 50+ platform integrations in production
AI Reliability & Governance
Making AI secure, measurable and trustworthy.
code audit / technical due diligence
SystemAudit
AI code reviews that sound confident but make things up, so founders can't tell real risks from noise
Every finding is checked against the real code, and anything it can't prove is deleted. You get only real risks, ranked critical to low, each with the exact file and line. Live at systemaudit.dev
- Pharma security engagement - 22 of 22 deliverables with evidence, 2 critical vulnerabilities fixed
- Public hallucination benchmarks - every accuracy claim auditable on /proof
22/22
contracted deliverables completed
confidential pharma engagement - each deliverable backed by an evidence artifact
Technical Due Diligence & Architecture
Helping leadership make the right AI decisions before writing code.
pharmaceutical / regulated SaaS
Enterprise Pharma AI - Security & Compliance
A prototype AI SaaS with cross-tenant data leakage, blocking enterprise pilot approval
Cross-tenant document endpoint fixed, prompt-injection guardrails shipped, approved for enterprise pilot
- Fractional CTO engagements - architecture reviews and AI strategy for funded teams
- 20-agent LLM ensemble - 38,319 agent predictions scored against market outcomes
· client record
platform-verified · 5.0 avg across engagements
Don't Take My Word For It
Hear directly from the teams who shipped production AI with Nic.
✓ verified client
“Nic ramped up on a complex project in no time, took the helm of our developer team, and ensured top-tier quality at every step through clear priorities, solid architecture, and thorough code reviews. He consistently went the extra mile, delivered ahead of schedule, and communicated crisply and proactively. Reliable, solutions-driven, and a pleasure to work with. I recommend partnering with Nic without reservation and would gladly collaborate with him again anytime.”
Chris N.
CEO, Enterprise Client
✓ verified client
“Nick is incredible, responsive and a joy to work with. I know I can give him a task, or a general direction, he will define and execute. I’d work with him again in a heartbeat (and we are signing our next contract now)! Highly recommend”
Tyler G.
Repeat Client, Enterprise SaaS
✓ verified client
“Goes above and beyond what is expected and provides expertise to the project that I didn’t think about. Super happy having Nic completing this project.”
Brett
Founder, CoachIQ
✓ verified client
“Nick is amazing! Incredibly knowledgeable, personable, helpful and practical in his approach. He quickly understands the brief and gives you the best way forward in a way that easily breaks down the problem and offers practical and actionable solutions. I couldn’t recommend him enough!”
Esuga A.
CEO, Law Firm
· live test
Ask the AI Architect
This assistant answers from a curated record of my shipped projects and published pages, and links the page each answer comes from. Ask anything before booking - it answers with specifics, not marketing.
NICCHIN-AI · loads on demand - keeps this page fast
live
Talk to Nic's AI assistant
Ask about multi-agent systems, RAG architecture, or case studies. The assistant loads on demand to keep this page fast.
What You Can Ask About
Your business problem
Describe the pain point (document overload, repetitive work, inconsistent content) and get a real case study back
How AI actually ships
Ask what it takes to get a custom AI system from idea into production, starting with a two-week discovery sprint
A specific project
Dig into SureCiteAI (multi-tenant RAG SaaS), SystemAudit (codebase intelligence), the 20-agent trading ensemble, or the legal document RAG
Cost, timeline, and fit
Talk through what an engagement looks like, what it costs, and whether I'm the right match
Technical Depth Guaranteed
This AI assistant provides the same level of technical detail and business insight I deliver in paid consulting engagements. Test my expertise risk-free.
Try These Questions
Pick one and it opens the assistant with the question already typed.
“Our AI pilot works in demos but keeps breaking with real users. What's usually wrong?”
“How did you stop cross-tenant data leaks in a multi-tenant RAG SaaS?”
“What does a production AI engagement with you actually look like, from discovery to launch?”
· engage
Talk to Nic
A 45-minute architecture conversation. No pitch deck. Bring your hardest AI question.
How an engagement starts
- 01A 45-minute call, at no charge. Pick a time in the calendar. The call starts at the architecture level, not discovery basics.
- 02Or the 2-minute AI intake. Tell the AI Architect your pain point, timeline and context. I read every intake myself and reply within 24 hours.
call at no charge · paid, fixed-price work from £600
Not sure what to write? Start from one of five openings and fill in the brackets.
Prefer email? nic.chin@nicchin.com
· starters
copy · edit · send
Don't start from a blank box
Pick whichever is closest, fill in the brackets, send. Each one already carries what a first call would spend ten minutes collecting - who you are, the problem, the deadline. Copying drops it into the chat too, so the intake opens with your message typed.
Writing your own? Rough is fine. Worth including: who you are (name, company, email), what you're trying to solve or save time on, where it stands today - idea, prototype, or already in production - and when it needs to be live. The AI Architect asks for whatever you leave out.