You don't have a blank page. You have a working product, an existing stack and a specific feature you want AI to do inside it. We build AI features that live inside your product, not next to it: predictions in your dashboard, vision in your warehouse, extraction in your back office, search in your app, classification in your pipeline.
We're a London-based AI engineering team working with CTOs, VPs of Engineering and technical founders at companies that already ship a product. 50+ AI features in production, fastest deployment two months from kickoff to live, and a reputation for telling you when an AWS API is enough.
AI built into your existing stackArchitecture matched to your latency budgetDrift monitoring from day oneHonest about when pre-built wins
A model in a notebook is not a feature. The gap between 'works in the playground' and 'runs reliably inside your existing auth, DB, compliance and monitoring stack' is where most projects die. We design for production from week one: API contracts, data pipeline, observability, fallback paths, drift detection. The model serves real users by the end of the build, not six months after.
800 milliseconds of model time on top of your existing search makes the feature unusable. Optimised ONNX hits 40-100ms p90; unoptimised PyTorch lands in the hundreds; each microservice hop adds another 8-20ms. We measure end-to-end p95 against your latency budget before choosing the architecture, not after. Sometimes the answer is async or batch. Sometimes it's edge.
Models degrade as your data changes. Standard accuracy metrics often miss it because the issue is multivariate: feature correlations shift while individual distributions look fine. We ship every model with confidence-distribution monitoring, golden-dataset regression tests, drift alerts and a retraining cadence. The system tells you it's slipping before your customers do.
The best custom AI is the one you didn't need to build. We start by testing the obvious pre-built option (AWS Rekognition, Google Vision, Azure Document AI, OpenAI) on your real data. If accuracy holds, volume is moderate and the per-call price scales, use it. If pre-built tops out below your bar, costs explode at volume, or vendor models change behaviour overnight, we build custom. We've stopped builds when pre-built was good enough.
Are you looking for these services?
We map the problem against your data, your existing stack, your latency budget and your regulatory exposure. Discovery includes a paid PoC on your real data when integration risk is high: testing the pre-built option, measuring real accuracy, surfacing the integration friction.
Output: architecture decision (sync, async, edge or hybrid), data pipeline plan, API contract, monitoring plan and a fixed-scope build quote.
Build runs as a small senior team led by Michal Vavra, with AI engineers and product engineers embedded with your team. We build the AI layer and the product code that surrounds it: API endpoints, data ingestion, caching, fallback rules, audit logging.
Each release ships behind feature flags with output validation, regression tests on a fixed eval set, and rollback in place from week one.
Latency optimisation (model distillation, ONNX conversion, caching). Cost controls (model routing, semantic caching, batched inference). Hybrid rule-plus-ML where determinism matters. The hybrid pattern is what experienced teams default to: rules first, ML on edge cases, both monitored. Cuts call cost, cuts drift exposure, kills 'confident wrong' answers.
At launch we wire in confidence histograms, embedding-distribution drift detection, accuracy regression on a golden set, cost telemetry and alerting. You receive runbooks, architecture documentation and a handover session.
Monthly retainer for monitoring and tuning, not a lock-in. Take the system in-house whenever you're ready.
The questions CTOs and VPs Engineering ask us in the first call about <strong>AI integration, latency, drift and run-rate cost</strong>.
If your task is standard, your data is clean and your volume is moderate, those services often win and we'll say so. Custom development pays off when pre-built accuracy doesn't hold on your real data, per-call cost explodes at volume (one camera-feed hit around $2,280/month on Rekognition), latency or privacy rules out the API, or you need control over drift and versioning. We test the pre-built option on your data first.
Yes. We work with the architecture you have rather than asking you to replace it. Sync API for real-time features, async batch for nightly scoring, edge for privacy or sub-50ms latency, hybrid rule-plus-ML for determinism on regulated paths. The architecture is chosen by your latency budget and traffic profile, not by what's easiest to ship.
Models degrade. Standard accuracy metrics often miss it because the failure is multivariate. We ship every model with confidence-distribution monitoring, embedding-drift detection, regression tests on a golden dataset, and a defined retraining cadence. You get a dashboard and an alert before customers notice. Maintenance retainer covers the work, or we hand it over.
Real numbers from production: optimised ONNX hits 40-100ms p90 on CPU; unoptimised PyTorch lands in the hundreds; each microservice hop adds 8-20ms. We measure p95 end-to-end against your latency budget before picking the architecture. If the budget is tight, we move to model distillation, caching, async pre-computation, or edge deployment.
Discovery is fixed-fee from £2K. Production integrations typically start at £10K and run six to ten weeks. Run rate depends on traffic and architecture: hosting, inference (with routing and caching), monitoring. We quote both numbers in the proposal so the run-rate isn't a surprise at month six.
Often, yes. Pre-trained models, fine-tuning on small labelled sets and synthetic data generation can take you a long way. We assess your data quality and volume in discovery and tell you honestly whether you have enough for a useful model, whether labelling effort is required, or whether the use case isn't viable yet. We've recommended fixing the data warehouse before funding a model.
You own the code, models, prompts, infrastructure-as-code and documentation. Standard contract is full IP transfer with no rented layer we hold back. After launch you can keep us on a monthly retainer for monitoring, drift and retraining, or take it in-house with a runbook and a handover session. Not a dependency.
