AI Platform
AI platforms that survive contact with production
Most AI demos break the moment real users, real data and real auditors show up. We build the platform underneath — so your GenAI and ML features ship reliably, securely and at known cost.
What we build
RAG & knowledge platforms
Document ingestion, chunking, embeddings, vector stores (pgvector, OpenSearch, Vertex Vector Search) and retrieval pipelines.
Model serving
Bedrock, Vertex AI, OpenAI, Anthropic — or self-hosted on GPU clusters with vLLM / Triton. Routing, fallback, rate limiting.
LLM Ops
Prompt versioning, eval pipelines, regression suites, telemetry, cost tracking, drift detection.
Guardrails & safety
Input/output filtering, PII redaction, jailbreak detection, audit logging — wired into your existing IAM and logging stack.
Agentic workflows
Tool-using agents grounded in your systems, with observability and human-in-the-loop checkpoints.
MLOps for classical ML
Training pipelines, feature stores, model registries, deployment patterns — SageMaker, Vertex, or Kubeflow.
Outcomes we target
- → A reference architecture your engineers can extend without asking us.
- → Cost per query you can actually predict and budget.
- → Evaluation that catches regressions before customers do.
- → An audit trail that holds up under InfoSec and (where relevant) FCA / EU AI Act scrutiny.
Engagement models
AI Readiness Review
1–2 weeks. Use-case triage, data audit, build vs buy, costed roadmap.
Pilot to Production
Take one prototype to a real production deployment — platform, evals, guardrails included.
Platform Build
Build the shared AI platform your product teams will use for the next two years.
Have an AI workload that needs to leave the prototype stage?
Tell us about the use case — we'll come back with a candid view.
Start the conversation