Production AI, engineeredby people who've shipped it.
FlintLark builds multi-agent systems, RAG pipelines, and LLM-powered platforms that go to production and stay there. Co-architected the AI engine behind Skylar — used by B2B teams at Databricks-tier companies. Live across 30+ languages and 1,500+ practice calls a week.
- Dubai · London
- 10+ years engineering
- 15+ LLMs in production
- Skylar · Lloyd's · Checkout.com
Engineering that has shipped at scale
Most AI prototypes don't survive contact with production.
You've seen the pattern. A weekend prototype impresses the demo, then collapses under real users, real data, and real compliance. Hallucinations, runaway token costs, brittle prompts, no observability, no guardrails. We have spent the last three years shipping AI systems that handle the boring parts — uptime, latency budgets, fallbacks, audit trails, EU AI Act readiness — so the impressive parts actually compound.
Versioned prompts
Every prompt is checked in, reviewed, A/B-tested, and rolled back when it regresses. Not a Notion doc that nobody opens.
Eval harness in CI
Before features land, we run them against a real eval set. No more 'it worked on the demo and broke for the customer'.
Guardrails by default
NeMo Guardrails, prompt-injection defence, PII redaction, EU AI Act risk classification. The compliance work is the work.
AI work we take on.
Six engagement types. All shipped to production for paying clients.
Multi-agent systems
Autonomous agents that plan, call tools, hand off to each other, and recover from failure. LangGraph, CrewAI, AutoGen, and our own orchestration patterns proven in customer-facing products.
RAG architectures
Document retrieval that actually retrieves. Hybrid search, re-ranking, citation-grounded answers, pgvector or Pinecone or Qdrant — chosen on engineering merit, not vendor pitch.
LLM-powered platforms
Full products with AI at the core: chat platforms, content engines, sales coaches, underwriting copilots. Skylar, MeowGTP, and a Lloyd's syndicate underwriting assistant are live examples.
Fine-tuning & evaluation
LoRA fine-tuning, dataset curation, eval harnesses in CI, A/B testing across Claude, GPT-5, Gemini 3, Llama 4, DeepSeek, and Mistral. The model that wins your task — not the one with the loudest benchmark.
AI compliance & safety
NeMo Guardrails, Guardrails AI, prompt-injection defence, PII redaction, EU AI Act risk classification, full audit trails. For insurance, payments, and government work where 'good enough' is not.
Fractional AI CTO
Architecture audits, strategy sprints, hiring panels, vendor selection. For boards that need a senior perspective without a full-time hire.
An engagement that actually ships.
Four phases, transparent at every step. Weekly demos. CI from day one. The hardening phase most agencies skip is the one we lean on hardest.
Discovery
1–2 weeks, paid. Architecture, data audit, model selection, eval plan. Output: a spec you could hand to anyone.
Build
4–10 weeks. Weekly demos. CI from day one. Eval harness before features. No silent rewrites.
Hardening
2–3 weeks. Load tests, guardrails, observability, runbooks. The part most agencies skip — and the part that decides whether the AI stays live.
Live & embedded
Retainer-based ownership or a clean handover to your team. We don't disappear after launch.
Production AI we've shipped.
Skylar
AI Sales Coach used by Databricks-tier B2B teams
1,500+ practice calls/week · 30+ languages
Co-architected and built the core AI engine for Skylar — used by B2B teams at companies like Databricks and Wove. LLM orchestration powering role-play personas trained on real buyer interviews. RAG pipeline ingesting company-specific playbooks and objection libraries. Real-time NLP feedback across 30+ languages.
Leading Lloyd's of London Syndicate
Underwriting Copilot at global scale
Reduced manual analyst workload · NDA
LLM-powered automation across an algorithmically underwritten broker platform at global scale. RAG pipelines for policy-document analysis, NLP-based broker submission parsing, decision-support flows for underwriters.
MeowGTP
Multi-Model AI Chat Platform
15+ LLMs · Thousands of conversations daily
Unified chat interface across 15+ LLMs (GPT-5, Claude Opus, Gemini 3, Mistral, Llama 4, DeepSeek). Real-time streaming, intelligent model routing, usage analytics, and PurrSafe — a multi-layer LLM classifier acting as a family-friendly content guardrail.
The AI stack — picked for engineering reasons.
Not for sponsorship deals. We benchmark before we build, and we hold the receipts.
Models
Claude · GPT-5 · Gemini 3 · Llama 4 · DeepSeek · Mistral · Hugging Face
Orchestration
LangGraph · LangChain · CrewAI · AutoGen · MCP · LlamaIndex
Retrieval & Vector
Pinecone · Weaviate · Qdrant · pgvector · Haystack
Safety & Ops
NeMo Guardrails · Guardrails AI · MLflow · Weights & Biases
Four reasons clients pick us.
Senior engineers, not seat-fillers
Every engagement is led by someone who has shipped at Checkout.com, BT, a Lloyd's syndicate, or all three. No junior-led delivery.
AI-fluent by default
We ship AI in production, not in slides. 15+ LLMs live in our work today, with eval harnesses and guardrails as table stakes.
Flexible delivery
Embedded partner when you need a team. Single principal when you need surgery. We scale up and down to match the work.
Outcome-clear pricing
Discovery sprints are fixed-price. Builds are milestone-based. No hourly games, no surprise change orders, no lock-in.
How AI engagements price.
Discovery Sprint
Architecture, data audit, model selection, eval plan. Output: a build spec, a cost model, and a go/no-go you can take to a board.
Scope a discovery sprintBuild Engagement
Full build with weekly demos, CI from day one, hardening phase, and a clean handover or retainer transition. Most AI builds land between $50k–$190k.
Talk through a buildEmbedded Retainer
Ongoing AI engineering ownership. Eval harnesses, model upgrades, cost optimisation, feature work. The senior partner your team can lean on.
Discuss a retainerAll figures starting points. Final scope and price set in discovery.
Questions we get asked.
We are not competing on price. The team behind FlintLark has shipped payments infrastructure used by Netflix and ASOS, telco platform tooling at BT, and AI underwriting at a Lloyd's syndicate. If your project can fail safely with junior engineering, we are probably not the right call. If it cannot, we are.
Whichever wins on your task. We benchmark across Claude, GPT-5, Gemini 3, Llama 4, DeepSeek, and Mistral as part of every build. The right answer is almost never 'one model for everything' — it is a routing layer with fallbacks and cost-optimised fan-out.
Yes — and we often do. Most engagements start with an audit of what you have, what is salvageable, and what needs replacing. We are explicit about it instead of quietly rebuilding everything.
Built in from day one for regulated clients. NeMo Guardrails or Guardrails AI for runtime safety, structured logging with retention policies, PII redaction at ingest, and EU AI Act risk classification baked into the spec. We have done this for a Lloyd's syndicate; we can do it for you.
A two-week discovery sprint from $20,000. Below that, you are better served by a contractor. We do not do hourly work, and we do not take projects we cannot ship properly.
Discovery sprints are always fixed-price. Build engagements are usually outcome-based with milestone billing — close enough to fixed-price that you can plan your runway, flexible enough to absorb the unknowns AI projects always have.
Dubai HQ, regular London presence. We take clients across the UAE, UK, US, and EU. Async-first day-to-day, on-site when it matters.
Have an AI project that has to ship?
Tell us what you are building. We respond within one business day, and we tell you in the first call whether we are the right team — or who is.
Typical reply: within 1 business day · EMEA business hours · petar@flintlark.com