AI-Powered Apps
Companies deploying AI at scale are reporting 6.2× ROI — and the gap between early movers and late adopters is widening every quarter. We design and build custom LLM-powered products: RAG knowledge bases, AI agents, streaming chat interfaces, and fully automated workflows on production-grade, observable infrastructure.
Everything you need
Custom LLM Integrations & Structured Tool Use
We connect Claude and GPT-4o to your internal systems via structured tool use and function calling — turning raw model intelligence into deterministic, auditable, and repeatable business workflows that produce consistent output you can stake operations on.
RAG Pipelines & Proprietary Knowledge Bases
Retrieval-Augmented Generation pipelines that chunk, embed, and index your documents, PDFs, databases, and internal wikis into Pinecone — so every model response is grounded in your proprietary data rather than a model's potentially outdated training.
AI Agents & Multi-Step Workflow Automation
Multi-step autonomous agents that plan, call external tools, and complete complex tasks end to end — from research pipelines and structured data extraction to customer support bots and internal process automation that runs without human handholding.
Real-Time Streaming AI Interfaces
Streaming UI components built with the Vercel AI SDK that surface model responses token by token — giving users the responsive, real-time feel of interacting with a frontier model directly, embedded naturally inside your product rather than as a bolted-on chat widget.
Systematic Prompt Engineering & Evaluation
Structured prompt design, few-shot example construction, chain-of-thought scaffolding, and a quantitative evaluation harness that measures accuracy, output consistency, and safety across every model version update — so performance is measured, not assumed.
Safety, Guardrails & LLM Observability
Structured output validation with schema enforcement, hallucination mitigation patterns, content filtering, rate limiting, and full LLM observability via LangSmith or Helicone — monitoring cost, latency, and output quality per call in production so problems surface before users report them.
Our process
Use Case Audit & Architecture Fit
We audit your data, map your specific use case to the right model and architecture — RAG, fine-tuning, structured tool use, or autonomous agents — and define quantitative success criteria before a line of code is written, so the project has a measurable target rather than a vague ambition.
Technical Architecture Design
We design the full technical architecture — model selection, embedding and chunking strategy, vector store configuration, tool and function definitions, memory and context management, and integration points with your existing systems — producing a written spec reviewed with you before engineering begins.
Iterative Build with Eval Suite
We build the pipeline iteratively — engineering and testing prompts systematically against an evaluation harness, implementing retrieval and tool use incrementally, adding safety guardrails and structured output validation, and measuring accuracy and consistency against real inputs before any user ever sees a response.
Deploy with Full Observability
We deploy to production with CI/CD, wire up LLM observability via LangSmith or Helicone to track latency, token cost, and output quality per call, configure rate limiting and graceful fallback logic, and run a structured optimisation cycle through the first 30 days based on real production traces — not synthetic test data.
How we build it
Common questions
We build primarily with Anthropic's Claude and OpenAI's GPT-4o — but we're genuinely model-agnostic, and model selection is always driven by what performs best for your specific use case rather than familiarity or preference. We benchmark candidate models against your actual data and tasks before committing to an architecture, and we design systems that can swap models as the landscape evolves.
Not necessarily. RAG pipelines work well with even modest, well-curated document collections — quality and structure matter more than volume. Fine-tuning requires more data but is often unnecessary if RAG and prompt engineering can achieve the required behaviour. We assess what approach fits your situation on the scoping call and won't recommend fine-tuning if simpler architectures will do the job.
Reliability and safety are engineered in from the architecture phase, not added after. We implement structured output schemas with validation, hallucination mitigation patterns, content filtering appropriate to your user base, human-in-the-loop checkpoints for high-stakes decisions, rate limiting to control costs, and a quantitative evaluation harness that measures model accuracy across a representative test set — so you can see exactly how the system performs before it goes live.
Yes — integrating AI into an existing product is one of our most common engagements. We assess your current stack, identify the right integration points, and build AI features that connect to your existing data and workflows via API. In most cases, the existing application continues running unchanged while new AI-powered features are added incrementally alongside it.
Your next digital product
starts here.
Tell us what you're building. We'll respond within 24 hours with honest advice — and a clear path forward.
Start my project →