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Outdated context and weak retrieval controls produce confident but incorrect outputs in critical workflows.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, teams 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 teams 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 teams 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 teams with failing LLM integrations
Enterprises upgrading knowledge workflows
Delivery sequence
How We Build Production-Grade AI Systems
A structured cycle that balances speed with reliability and governance, so AI capabilities remain operable after launch.
Discover
Audit your workflows, systems, and constraints to identify high-value AI opportunities.
Architect
Design model-routing, retrieval, guardrails, and observability around your real SLAs.
Implement
Build production-ready pipelines, APIs, and orchestration integrated with your stack.
Evaluate
Measure quality, latency, and cost with automated benchmarks and failure analysis.
Operate
Ship with monitoring, feedback loops, and change controls to keep systems reliable.
Selected proof
Case Study Preview
Examples of how we turn fragile prototypes and underperforming retrieval systems into governed production workflows.
Support automation
Support Automation for a B2B SaaS Platform
Implemented an agent orchestration layer with retrieval grounding and escalation logic to reduce manual triage and improve response consistency.
Result
Production rollout with observable quality controls and reduced operational load.
Retrieval upgrade
Knowledge Retrieval Upgrade for Internal Operations
Rebuilt ingestion and retrieval flow with freshness controls, access policies, and evaluation gates for high-stakes internal workflows.
Result
Improved retrieval precision and stronger audit readiness.
Latest thinking
Featured Insights
Architecture notes on evaluation, platform reliability, retrieval, and agent operations for teams shipping AI in production.
AI Evaluation in Production in 2026
Why serious AI teams now treat evaluation as a delivery system, not a benchmark spreadsheet.
Observability for Agent Systems
Agent systems become operationally expensive when teams cannot see where reasoning, tools, or retries are failing.
RAG Architecture That Survives Scale
Retrieval systems break long before models do if freshness, permissions, and ranking strategy are not engineered from the start.
Working model
Engagement Models
Choose a delivery model based on certainty, timeline, and execution depth. We can start small and scale into full implementation.
Architecture Sprint
2–4 week discovery and technical design for teams de-risking a critical AI initiative.
Primary Path
Build Partnership
Multi-month implementation with your engineering team on core AI system and platform delivery.
Advisory Retainer
Ongoing architecture and governance support for teams operating production AI at scale.