Production AI should improve with scale, not degrade
Production AI often degrades when real users, changing data, longer workflows, and higher traffic expose weaknesses across prompts, retrieval, agents, tools, and infrastructure.
We trace the full system to find the bottlenecks affecting quality, latency, cost, and reliability, then improve only the layers that will make a measurable difference.
What is limiting your AI system
Every AI system has a weakest layer. It usually stays invisible until scale, data, or user behaviour finds it.
Inconsistent quality
Outputs change across real inputs, longer conversations, and new data. We isolate whether the failure begins in the model, prompt, retrieval, tool, or source data.
High token and inference costs
Oversized prompts, duplicated context, repeated retrieval, and unnecessary model calls make spend grow faster than usage.
Slow response times
Time accumulates across retrieval, reranking, model calls, agent loops, tools, and validation, making the entire experience feel slow.
Unreliable agents
Agents lose state, misuse tools, loop, or fail without recovery. Model upgrades rarely solve the underlying harness problem.
Weak observability
Logs show that a request failed, but not where the failure began. Missing traces turn diagnosis into guesswork.
Difficulty scaling
Pilot architecture strains under higher traffic, larger data, more integrations, and longer workflows, eventually blocking the roadmap.
Optimize every layer around the model
We work within your existing stack and focus engineering effort where it will have the greatest impact.
Context engineering
Optimised data ingestion and retrieval pipelines to provide engineered context for long-horizon or latency-sensitive tasks, enabling cost-effective contextual agents.
Context layer
Agent
Harness engineering
Purpose-built, lean agent scaffolds that enable models to complete tasks through effective tool design, tailored integrations, memory, and observability for continuous improvement.
Model selection & routing
Identify the best-fit models and intelligently route requests between them based on complexity, enabling applications to scale cost-effectively without compromising quality.
User request
Analysing query complexity
Model routing
GPT-5.6 Sol
Complex reasoning
Qwen 3.5 9B
Simple queries
Fine-tuning & distillation
Develop task-specific models that match or outperform expensive SOTA models through synthetic data expansion and policy optimization, enabling continuous improvement.
Secure data access
Scale features across large user bases with permission-aware harnesses that deliver the right context to each user while keeping sensitive data secure.
User query
What was Q3 revenue?
Employee 1
Marketing team
Campaign spend and pipeline only. Revenue figures withheld.
Employee 2
Finance team
Q3 revenue $4.2M, up 18% against Q2, with segment breakdown.
User query
What was Q3 revenue?
Employee 1
Marketing team
Campaign spend and pipeline only. Revenue figures withheld.
Employee 2
Finance team
Q3 revenue $4.2M, up 18% against Q2, with segment breakdown.
How we optimize your production systems
We integrate with your existing infrastructure to deliver measurable improvements without draining your internal engineering capacity.
1. Instrumentation and tracing
We implement observability across real production traffic to track token flow, latency bottlenecks, tool failures, and context degradation. This establishes a representative, data-driven performance baseline.
2. Targeted engineering
We isolate the layers causing hallucinations, latency spikes, or growing costs, then make focused architectural changes in an isolated environment, from prompt compaction and RAG refinement to deterministic state machines.
3. Validation and handoff
We prove the improvement against the baseline, implement regression evaluations, hand over the optimized architecture, and equip your team to ship future updates safely.
Built for measurable business outcomes
Systems we have shipped, and the results they delivered.
Improve what is already in production
Common questions about optimizing an existing production AI system.