Across industry data, roughly 85% of LLM prototypes never make it to production. The notebook demo is easy. The system that holds up under real traffic, real edge cases and real data drift is the actual work. We build LLM-powered features that ship, scale and stay up past launch day.
We're a London-based AI engineering team working with CTOs, VPs Product and technical founders. 50+ AI features in production, fastest deployment two months from kickoff to live, and a reputation for telling you when an LLM isn't the right tool for the job.
Prompt, RAG or fine-tune decided honestlyProduction-first, not PoC-onlyVendor-portable by defaultPost-launch cost transparency
Teams waste weeks and tens of thousands of dollars training a model when the real gap was a prompt or a retrieval bug. Our default order is prompt engineering first (days, near-zero cost), RAG if knowledge is the gap (weeks), fine-tuning only as a last resort for stable high-volume behavioural change (months, real money). We make that call with you in writing, with the trade-off explained.
A solo dev's RAG chatbot went from $20 to $300 a month at 50 users because every query hit the frontier model. A doctor-appointment chatbot claimed to have booked appointments that never happened. These are not marketing failures. They are missing output validation, missing deterministic middleware, missing model routing and missing evaluation. We engineer the mitigations in from day one.
Skepticism that agencies are 'just OpenAI API wrappers' is fair. We design with a model abstraction layer, model-agnostic eval pipelines and an architecture that lets you swap Claude, Gemini, Llama or a self-hosted model without rewriting the app. When a provider deprecates an endpoint or changes pricing, you have options.
Sometimes the right answer is a rules engine, a classifier or a SQL query. If your problem has a deterministic ground truth, tight latency budgets or strict audit requirements that an LLM can't honestly meet, we'll say so and point you to the cheaper, more reliable tool. We'd rather not build it than build the wrong thing.
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We map the use case against your data, your latency and accuracy targets, your integration surface and your regulatory exposure. The deliverable is a decision on prompt vs RAG vs fine-tune, an integration plan, a privacy architecture and a fixed-scope build quote.
We'll tell you up front if the use case isn't LLM-shaped. We have done this on our own engagements.
Where retrieval or integration risk is high we run a time-boxed PoC on your data, your APIs, your IAM. The PoC is designed to fail fast on the things that usually kill LLM projects: chunking strategy, retrieval quality, permission propagation, schema drift.
You get an evaluation report with specific metrics against a golden set and a concrete build quote.
Build runs as a small senior team led by Michal Vavra, with AI and integration engineers embedded with your stakeholders. Each release ships behind feature flags with eval pipelines running in CI, output schema validation, regression tests on a fixed golden set, and cost telemetry in place from week one.
We deploy early. Gated on guardrails and evals, not on demo polish.
At launch we wire in faithfulness tracking, drift detection, token and inference cost telemetry, output validation and alerting. You receive runbooks, architecture documentation, the full eval harness and a handover session.
Monthly retainer for monitoring, tuning and model updates, not a lock-in. Take the system in-house whenever you're ready.
The questions technical buyers ask us in the first call about <strong>LLM development, RAG, fine-tuning and run-rate cost</strong>.
Our default order is prompt engineering first, RAG if knowledge is the gap, fine-tuning only as a last resort. Around 90% of production wins are prompt plus RAG. Fine-tuning is the 10% exception that still gets overused, usually because a team assumes the model needs more training when the real gap is bad retrieval or a weak prompt. We make that call with you in writing during discovery.
The build and the run rate are two different conversations. Inference cost scales with token volume, model choice and whether you have routing and caching in place. A naïve deployment on the frontier model can easily multiply its own projected cost at launch: we've seen a jump from $20 to $300 a month at 50 users from a single missed routing decision. We quote a projected monthly run rate in the discovery proposal and design model routing, semantic caching and evaluation gates in from the start.
We treat hallucination as an engineering problem, not a marketing one. Mitigations are layered: output validation against typed contracts, retrieval quality controls, deterministic middleware that pulls authoritative state before the model sees it, human-in-the-loop on irreversible actions, and faithfulness scoring tracked across every release. We don't claim '100% reliable' or 'never hallucinates'. We measure the rate and ship mitigations until it's acceptable for your use case.
We build with a model abstraction layer and model-agnostic eval pipelines from the start. Swapping GPT to Claude, Gemini, Llama or a self-hosted model is a configuration change plus an eval re-run, not a rewrite. You're not locked to any one vendor, and we'll rotate if the economics or policy change.
Yes. We support Azure OpenAI with zero retention, private cloud deployments on AWS or GCP, and self-hosted open-source models on your own infrastructure. The choice is driven by your data residency, confidentiality and volume requirements. We run a TCO comparison at your projected volume and tell you honestly if self-hosting is worth the operational overhead.
The model is the easy part. The production system is retrieval, output validation, eval pipelines, model routing, caching, monitoring, fallbacks, guardrails and a CI-integrated regression harness. That's what we build. If your use case genuinely is a single API call, we'll tell you to wire it up yourselves.
When the problem has a deterministic ground truth, tight latency budgets, or strict audit requirements the LLM can't honestly meet. Classification with clean labels, exact-match search, transactional state: these are often better served by a classifier, SQL or a rules engine. We'd rather point you to the cheaper, more reliable tool than build the wrong thing.
