Intelligent Systems
AI that ships, not AI that demos.
We build the unglamorous parts of AI — the pipelines, the eval harnesses, the inference layer, the cost controls — so the parts your users see actually work, at scale, in production.
Engagement scope
What you get
- End-to-end ML pipelines: training → evaluation → serving
- LLM integration with proper prompt engineering, eval, and guardrails
- Real-time inference engines with sub-100ms latency targets
- Vector search and retrieval-augmented generation (RAG) systems
- Cost monitoring, fallback chains, and graceful degradation
- Full observability: tracing, drift detection, and offline replay
Capabilities
Where we go deep
ML Pipeline Architecture
Reproducible training pipelines with versioned data, models, and experiments. Automated retraining triggers tied to drift signals — not calendars.
LLM Integration & Fine-Tuning
Production-ready integration with OpenAI, Anthropic, and open-source models. We handle eval, A/B testing, prompt versioning, and cost controls so you don't get a surprise bill.
Real-Time Inference Engines
Low-latency serving with proper batching, caching, and autoscaling. We've shipped inference paths that hold p99 under 100ms at production load.
Vector Databases & RAG
Pinecone, pgvector, Weaviate — chosen based on your data, not the demo. Hybrid search, metadata filtering, and reranking when it earns its keep.
Predictive Analytics
Forecasting, anomaly detection, and recommendation systems embedded directly in your product surface — not stuck in a Jupyter notebook on someone's laptop.
Technology
The stack we ship
Languages
- Python
- TypeScript
- Rust
ML Frameworks
- PyTorch
- JAX
- scikit-learn
- Hugging Face
LLM Providers
- OpenAI
- Anthropic
- Mistral
- Ollama / vLLM
Vector Stores
- pgvector
- Pinecone
- Weaviate
- Qdrant
Infra
- AWS / GCP
- Kubernetes
- Modal
- Docker
Observability
- Langfuse
- Weights & Biases
- OpenTelemetry
How we work
The engagement, end to end
Discovery
We start with the user problem, not the model. Two-week scoping engagement to align on success metrics, eval criteria, and risk surface.
Prototype
End-to-end thin slice: real data, real model, real serving — just minimal scope. We deploy something usable in weeks, not months.
Harden
Eval harness, observability, fallback chains, cost controls. The work that turns a prototype into something ops can sleep through.
Operate
Optional ongoing engagement: drift monitoring, retraining cadence, prompt updates, model upgrades as the frontier moves.
What we measure
Outcomes we hold ourselves to
<100ms
Inference latency p99
99.9%
Serving uptime SLA
30–40%
Typical LLM cost reduction via caching + routing
FAQ
Questions worth answering
Do you work with proprietary models or only OpenAI/Anthropic?+
Both. We pick the model after we understand the problem. For regulated industries or extreme cost sensitivity we frequently deploy open-weight models on dedicated infra. For general reasoning tasks the frontier APIs usually win on quality-per-dollar.
Can you take over an existing ML system?+
Yes. We frequently inherit codebases that grew organically. The first deliverable is usually an audit: what works, what's brittle, what needs to be rewritten, and a sequenced plan that keeps the system running while we improve it.
How do you handle model evaluation?+
Every system we ship includes a versioned eval set, automated regression tests on prompt or model changes, and a human-review queue for edge cases. No 'looks good to me' deploys.
Where does the data live?+
Wherever your compliance posture requires. We deploy in your cloud account, on dedicated infra, or in fully air-gapped setups when regulation demands it.
Ready to talk through your project?
We respond to every enquiry within one business day. Briefs, early-stage ideas, and architecture audits all welcome.