Delivery outcomes
Representative production improvementsProduction friction
Why AI Features Fail in Production
Most AI delivery problems are platform and systems problems first. Reliability breaks where architecture, evaluation, and retrieval discipline are weak.
Reliability Drift
Prototypes pass demos but collapse under real workload variance, error handling, and integration complexity.
Data Freshness Gaps
Outdated context and weak retrieval controls produce confident but incorrect outputs in critical workflows.
Evaluation Blind Spots
Without structured evaluation and observability, companies cannot predict quality, detect regressions, or defend outcomes.
Platform Debt
Synchronous AI calls and weak service boundaries create latency, cost volatility, and hard-to-debug systems.
Core capabilities
Core Engineering Services
Structured around production AI agents, the distributed platforms that run them, and the web, mobile, and internal interfaces where companies actually use them.
01
AI Systems Engineering
Agent architecture, orchestration, runtime controls, and evaluation pipelines for production AI features.
- Agent workflows and fallback strategy
- Online and offline evaluation loops
- Human-in-the-loop operational controls
02
Backend & Platform Engineering
Distributed backend foundations for reliable AI agent delivery across product, web, mobile, and internal operational environments.
- Service boundary and API design
- Agent runtime and delivery architecture
- Observability and incident readiness
- Latency, resilience, and cost controls
03
RAG & Knowledge Systems
Retrieval pipelines that ground answers in trusted sources with strong freshness, access, and governance controls.
- Ingestion and indexing architecture
- Retrieval quality and grounding strategy
- Multi-tenant knowledge access controls
Ideal partners
Who We Work With
We fit companies that already know AI is strategic and now need systems rigor, platform depth, and delivery accountability.
Funded startups scaling AI products
CTOs modernising legacy backend systems
Product-led companies with failing LLM integrations
Enterprises upgrading knowledge workflows
