AI engineering services for production systems.
We partner with teams to build AI that integrates, scales, and creates real impact — one standard across every surface: integrated, observable, evaluated, and built to operate.
Production first
We build systems designed for reliability, integration, and scale — not demos that stall before launch.
Measured outcomes
We instrument, evaluate, and iterate against the decisions and metrics the system is meant to move.
Engineering depth
Senior builders with deep AI, data, and systems experience across finance, industry, and media.
Agent Systems & AI Products
Agents that plan, call tools, and stay under control.
The business problem
Most “AI features” stall the moment they leave a demo. A single clever prompt can look impressive and still be impossible to operate — no guardrails, no observability, no way to explain why an output changed. Teams shipping agents into real products need systems that behave predictably under load and degrade safely when they don’t.
What we build
We design agent runtimes around explicit control flow — planner / worker / reviewer patterns, typed tool calling, and human-in-the-loop checkpoints where the stakes justify them. Outputs are structured and reproducible, execution is sandboxed, and every step is traceable. We’ve built systems that turn a business scope into a deployed backend service, generating agent graphs, tool configs, and an integration-ready API surface.
What good looks like
- Deterministic structure: the same input shape returns the same output shape, every time.
- Bounded autonomy: tools are typed, permissioned, and observable — nothing executes blind.
- Recoverable failure: timeouts, retries, and fallbacks are designed in, not bolted on.
- Operable from day one: traces, logs, and cost-per-run are visible to the whole team.
Typical deliverables
- Agent runtime with planner / worker / reviewer orchestration
- Typed tool layer and integration-ready API surface (FastAPI / TypeScript)
- Human-in-the-loop checkpoints and approval flows
- Trace, log, and replay tooling for every agent run
Retrieval, Search & Knowledge Systems
Grounded retrieval you can tune, observe, and trust.
The business problem
Retrieval is where most enterprise AI quietly fails. Answers drift, citations point to the wrong place, and no one can explain what the system retrieved or why. When your knowledge is messy, multi-tenant, or access-controlled, naive RAG produces confident nonsense — and erodes trust faster than shipping nothing at all.
What we build
We build retrieval layers that are engineered, not guessed: hybrid search that combines keyword precision with semantic recall, deliberate chunking and indexing strategies, and grounded responses with citations and traceability. Retrieval is instrumented end-to-end, so you can see what was retrieved, why it scored the way it did, and how it changed the answer — then tune it with evidence.
What good looks like
- Grounded answers: responses cite sources and say “I don’t know” instead of hallucinating.
- Hybrid by design: keyword precision and semantic recall both contribute and are tunable.
- Observable retrieval: every query exposes what was retrieved and how it ranked.
- Tenant-aware: persona and access patterns are enforced inside the retrieval layer.
Typical deliverables
- Hybrid (keyword + vector) retrieval pipeline
- Chunking, indexing, and re-ranking strategy tuned to your corpus
- Citation and traceability layer for grounded responses
- Multi-tenant access and persona controls
Voice & Multimodal AI
Real-time voice that feels human and reaches live data.
The business problem
Voice and multimodal raise the bar on everything. Latency is felt in milliseconds, interruptions must be handled gracefully, and the system has to reach into live enterprise data without falling over. The text patterns that work in a chat window do not survive contact with a real-time conversation.
What we build
We deliver real-time voice agents with natural, human-like interaction across 40+ languages for sales and support — including Gemini Enterprise deployments in partnership with Google Cloud. The work is in the engineering: low-latency speech pipelines, turn-taking and barge-in handling, and seamless integration with enterprise data and downstream systems.
What good looks like
- Latency budgets are explicit and held — the conversation feels human.
- Barge-in, turn-taking, and recovery are handled, not hoped for.
- Voice is grounded in live enterprise data, not a static script.
- Quality holds up across languages, accents, and channels.
Typical deliverables
- Real-time voice agent with low-latency speech pipeline
- Turn-taking, barge-in, and graceful fallback handling
- Enterprise data and telephony / channel integration
- Multilingual support with an evaluation harness
Related work
Applied ML & Computer Vision
Prediction and perception that hold up in the real world.
The business problem
Not every problem needs an LLM. Risk scoring, forecasting, recommendation, and perception are still won with applied ML and computer vision — but only when the data pipeline, evaluation, and deployment are treated as first-class. The hard part is rarely the model; it’s everything around it.
What we build
We build credit scoring and risk models for banks and large e-commerce platforms, bond default prediction, graph neural network recommenders for wealth management, and time-series forecasting that feeds operational planning. On the perception side, we’ve shipped edge and in-cabin monitoring (pose, emotion, object detection) for constrained environments, and large-scale metadata extraction from images and video.
What good looks like
- Models are evaluated against the decision they support — not just an offline metric.
- Pipelines are reproducible and continuously monitored for drift.
- Edge and embedded constraints are designed for, not discovered late.
- Outputs feed real operational systems and planning databases.
Typical deliverables
- Risk, scoring, recommendation, or forecasting models
- Computer vision and perception pipelines (incl. edge / embedded)
- Feature pipelines, training infrastructure, and drift monitoring
- Deployment into operational systems and data stores
AI Platform Engineering, MLOps & Reliability
The substrate that keeps AI reliable and affordable.
The business problem
AI that works in a notebook and AI that works for ten thousand users are different engineering problems. Without observability, cost controls, and rollout discipline, production AI becomes unpredictable and expensive — and no one notices quality regressing until a customer does.
What we build
We build the production substrate: model routing (cheap models for routine steps, premium models reserved for high-impact ones), caching, batching, and scheduling to control token burn, and cost + latency instrumentation per feature, tenant, and workload. We’ve managed end-to-end ML infrastructure and lifecycle for one of Europe’s largest mobility platforms, handling massive-scale ingestion and deployment.
What good looks like
- Unit economics are known: cost per run, per tenant, per feature.
- Quality is monitored continuously; regressions surface before customers do.
- Rollouts are staged behind feature flags with safe, fast rollback.
- The system is multi-tenant, observable, and — deliberately — boring to operate.
Typical deliverables
- Model routing and caching / batching for cost control
- Cost and latency instrumentation per feature / tenant / workload
- Observability, logging, and rollout (feature-flag) tooling
- Multi-tenant, scalable serving architecture
AI Discovery, Architecture & Delivery Strategy
De-risk the work before you commit a quarter to it.
The business problem
The most expensive AI projects are the ones that should never have started — or that started without a definition of success. Teams need a fast, honest read on feasibility, cost envelope, and the right architecture before they commit a quarter to a build.
What we build
We run discovery the way we run delivery. We align on workflows, acceptance criteria, quality targets, and cost envelope, then produce a reference architecture and an evaluation plan early. You get a defensible recommendation — including “don’t build this with AI” when that is the honest answer.
What good looks like
- Success is defined in measurable terms before a line of production code.
- Architecture and evaluation plan exist before the build, not after.
- Cost envelope and unit economics are estimated up front.
- You leave with a clear go / no-go, not a sales pitch.
Typical deliverables
- Problem framing, workflow map, and data audit
- Reference architecture and integration plan
- Evaluation plan with acceptance criteria and quality targets
- Feasibility assessment and go / no-go recommendation
What this looks like in production.
Not sure which service you need?
That’s what discovery is for. Tell us the problem and we’ll map it to the right work — or tell you if AI isn’t the answer.