AI woven in.
Not bolted on.
Production-grade generative AI, RAG pipelines, agentic workflows, and custom ML models. Intelligence designed into your architecture from day one.
The real problem
Most AI projects
never reach
production.
Not because of the models. Because of the architecture. Teams skip from "add AI" to "build model" without understanding what decision they're automating, or how to measure it.
We've seen this pattern. We fix it before writing a single line of model code.
AI bolted on as a feature
We build it as infrastructure from day one
No evaluation framework
RAGAS + regression suites before every deploy
Wrong tool for the job
Architecture-first diagnosis, not model-first
Production blindspot
Full-lifecycle engineering: launch is the start
What we build
10 capabilities.
One team.
Not a services menu, but an integrated engineering capability. Every item below is something we have shipped in production, not a slide deck service offering.
Generative AI engineering
Production LLM pipelines from prototype to deployment. Custom inference, prompt chains, structured output validation.
Intelligent chatbots & AI assistants
Context-aware conversational AI that handles edge cases, maintains session memory, and escalates intelligently.
Agentic workflows & multi-agent systems
Autonomous agents that plan, execute tool calls, and orchestrate complex multi-step tasks without constant supervision.
RAG pipelines & document retrieval
Retrieval-augmented generation over your proprietary data: accurate, fast, and source-cited answers at scale.
Custom ML model development
Trained models for classification, prediction, and detection problems where generic LLMs are the wrong tool.
LLM fine-tuning & evaluation
Adapting foundation models to your domain, tone, and task requirements with rigorous before/after measurement.
Vector embeddings & semantic search
High-performance similarity search over embeddings: recommendation engines, deduplication, and intelligent search.
Query optimisation & prompt engineering
Systematic prompt design, few-shot strategies, and latency/cost optimisation that makes AI viable at scale.
On-premise LLM deployment
Self-hosted open-source LLMs for data residency, compliance, and IP protection. No data leaves your infrastructure.
MLOps & model monitoring
Continuous evaluation, drift detection, and performance tracking so quality doesn't degrade silently in production.
How we deliver
The engineering
process we follow.
Problem framing
We define what AI needs to solve, specifically. Most failures happen here when teams skip from 'add AI' to 'build model' without understanding the actual decision being automated.
Architecture design
We design the AI layer as first-class architecture: inference endpoints, RAG vs fine-tuning decisions, vector store selection, latency budgets, and data pipeline layout.
Pipeline development
Build the full stack: embeddings, retrieval, reranking, LLM integration, output validation, and caching. Production-grade from the first sprint.
Evaluation & iteration
RAGAS-based evaluation, human eval samples, regression suites. We measure before we ship and track quality continuously after launch.
Technology
The production stack.
Every tool here has been used in production, not just evaluated. We select based on your requirements, not our preferences.
Foundation Models
Orchestration
Vector & Retrieval
Deployment
Evaluation & MLOps
Compute & Frameworks
FAQ
The questions
CTOs always
ask us first.
Straight answers. No marketing language.
AI & ML · Triage AI
Ready to build
production AI?
Tell us what you're building. We'll give you a specific architecture, team composition, and timeline. Not vague estimates.