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AI & LLM Solutions
That Actually
Ship.

We build production-grade AI systems — retrieval pipelines, agents, copilots, and ML integrations — engineered for accuracy, cost, and reliability, not just demos.

Scope an AI Project
What we build

From prototype
to production AI.

LLM Applications
Chatbots, assistants, and content tools built with production safety, cost controls, and observability.
RAG Systems
Retrieval-Augmented Generation over your data — properly evaluated, not prompt-engineered and hoped for.
AI Agents & Copilots
Tool-using agents for internal workflows, customer-facing automation, and knowledge work acceleration.
Document AI
Intelligent extraction, classification, and summarization for contracts, invoices, reports, and RFPs.
Embedded ML
Integrate trained models (recommendation, forecasting, classification) into existing software products.
AI Infrastructure & MLOps
Evaluation harnesses, model pipelines, observability, cost monitoring, and quality drift detection.
Technology

Stack we trust
for production AI.

LLM Providers
OpenAIAnthropicAzure OpenAILlamaMistral
Orchestration
LangChainLlamaIndexSemantic KernelCustom pipelines
Vector Stores
pgvectorPineconeCosmos DBAzure AI Search
MLOps
MLflowOpenTelemetryEvaluation harnessesCost monitoring
Cloud
AWS BedrockAzure AI FoundryAWS SageMaker
Why our AI projects don't stall

Evaluation first.
Production always.

01
Feasibility
Use case discovery, data readiness audit, and evaluation set design before any prompting.
02
Prototype
Build with an evaluation harness from day one — quality metrics before shipping anything.
03
Productionize
API layer, UX integration, safety guardrails, cost controls, and observability.
04
Operate
Continuous evaluation, quality drift monitoring, model swaps as better options emerge.
Common questions

AI development FAQs.

Should we use a closed model (OpenAI/Anthropic) or open-source?
Depends on your latency, cost, data privacy, and quality requirements. Closed models win on out-of-the-box quality; open models win on cost, privacy, and fine-tuning control. We'll give you a concrete recommendation after understanding your use case.
How do you measure AI quality?
We define evaluation criteria before writing any code — typically a combination of automated metrics (relevance, faithfulness, groundedness) and human eval. Every project ships with a repeatable evaluation harness.
What about data privacy when using LLMs?
We design with data minimisation, PII redaction, and private deployment options from the start. Azure OpenAI and on-prem model deployments are options when data sovereignty matters. We'll map the right approach to your compliance requirements.
Can we start with a small proof of concept?
Yes — a scoped PoC with an evaluation harness is often the right first step. It de-risks the project and gives you real quality data to make a build decision on.

Have an AI problem worth solving?

We build AI that ships to production — not just demos that impress in a meeting.

Talk to an AI Engineer