AI software development company in London
You don't have a blank page. An AI software development company should meet you there: a working product, an existing stack and one 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 work with CTOs and technical founders who already ship. They want AI software development that adds one feature inside the product they run today — not a new platform beside it.
AI built into your existing stack- Architecture matched to your latency budget
- Drift monitoring from day one
- Honest about when pre-built wins
When AI software development belongs inside the product
Features users rely on are not side experiments
If AI sits on the critical path, it needs product thinking: empty states, errors, latency, cost, and a rollback plan, not just a model call.
You cannot hack the trust layer in a weekend
Auth, tenancy, audit, evals, abuse cases. The bits users never see are usually why DIY AI features get pulled after the first incident.
We build with your stack, not against it
Design, app, and infra sit with the same team, so the AI feature does not become an orphan microservice nobody wants to touch.
Stay in experiment mode if…
Internal experiments are healthy. Production promises to customers are when you want a team that has shipped this before.
Are you looking for these services?
How we put AI in the product you already ship
Discovery and Architecture Decision (weeks 1-2)
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 into the product (weeks 3-10+)
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.
Production Hardening
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.
Launch, Monitoring and Iteration
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.
What AI software development costs
Frequently Asked Questions
The questions CTOs and VPs Engineering ask us in the first call about the feature, latency, drift and run-rate cost.
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 £2,000. Production integrations typically start at £10,000 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.
Related
Related AI work
If the model already works and production does not, that is AI integration services. This page is the feature inside the product you already ship.