AI applications that earn their place in your product.

Beyond demos and chatbots: copilots, semantic search, document understanding, vision pipelines and decision support — built with real evaluations, observability and a clear story for accuracy, cost and risk.

Overview

Most AI demos don’t survive contact with users.

The hard part isn’t getting an LLM to answer once. It’s making it consistent, fast, accurate, debuggable and economical — for thousands of users, on real data, week after week.

We design AI features the way we design any other production software: with measurable goals, evaluation suites, telemetry, fallbacks and a clear human-in-the-loop story for the tricky 5%.

Real evaluationsTest sets, scoring, regression tracking — not vibes.
Cost-aware designSmart routing across models, caching and prompt budgets that pay for themselves.
Safety & guardrailsInput validation, output checks, content policies and audit trails.
Humans in the loopReview queues, feedback capture and continuous improvement — from day one.
WHAT WE BUILD
  • In-app copilots and assistants
  • Semantic and hybrid search
  • Document AI & data extraction
  • Summarisation and report generation
  • Computer vision pipelines
  • Classification and recommendation
  • Voice and speech interfaces
  • AI-augmented workflows
Capabilities

The full stack of an AI feature.

Retrieval & grounding

RAG pipelines, embeddings, hybrid search and reranking that keep answers tied to real, sourced data.

Evaluation & observability

Eval suites, prompt regressions, latency budgets, traces and dashboards for live AI behaviour.

Model selection

Right-sized models per task — commercial APIs, open-source or on-prem — based on cost, latency and accuracy.

Safety & policies

Content filters, PII handling, jailbreak resistance and policy alignment with your domain.

Workflow integration

AI features wired into actual product surfaces — not bolted-on chat windows nobody opens.

Cost & throughput

Caching, batching, semantic routing and tier-aware fallbacks — AI that scales without surprises.

Process

How an AI feature actually gets built.

Define the win

What metric improves? Accuracy on what task? Time saved per case? We anchor on a measurable goal.

Build the eval

A small but realistic test set we can score automatically — the foundation for every iteration that follows.

Iterate the system

Prompts, retrieval, reranking, fine-tuning, tools — whatever gets the score up while keeping cost sane.

Ship & observe

Roll out behind feature flags, watch real usage, capture feedback and tune. The eval keeps regressions out.

Use cases

Where AI moves the needle.

Customer support

Self-serve answers grounded in your real docs and tickets, with confident escalation when needed.

Knowledge search

Search across documents, wikis, tickets and code — answers with citations, not just blue links.

Document AI

Extract structured data from invoices, contracts, forms and reports with verifiable accuracy.

Sales & marketing

Personalised outreach, content drafts, lead enrichment and live call assistants.

Vision pipelines

Image classification, OCR, defect detection, identity verification — from prototype to production.

Internal copilots

Domain-specific copilots for ops, HR, finance and engineering teams — saving hours every day.

Build with us

Have an AI feature you want to ship right?

We’ll review the use case, the data, and tell you honestly whether AI is the best tool — and how we’d build it.