Services

Agents and LLM applications on current models and open standards, with the evaluation and security work that gets them into production.

Agentic AI systems

Production agents that plan, use tools, and complete multi-step work across your systems. Built on current agent frameworks and open protocols like MCP and A2A, so they stay portable as models change.

  • Single and multi-agent designPlanner, worker, and reviewer agents orchestrated with LangGraph, CrewAI, or the OpenAI and Claude agent SDKs.
  • Tool access through MCPModel Context Protocol servers that give agents scoped access to your APIs, databases, and SaaS tools.
  • Human-in-the-loop controlsApproval steps, escalation rules, and autonomy limits set by the risk of each action.
  • Agent runtime and memoryState, long-running tasks, retries, and memory so work survives interruptions.
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LLM application development

Copilots, assistants, and document tools on the latest frontier and open-weight models, with routing so each task uses the right model for cost and quality.

  • Copilots and assistantsEmbedded in Slack, Teams, your CRM, or your own product.
  • Document intelligenceSummarize, compare, and draft from long documents with long-context models.
  • Model routing and cost controlRequests routed across GPT, Claude, Gemini, Llama, and others by task, latency, and budget.
  • Structured outputsReliable JSON and function calls so AI output plugs straight into your systems.
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RAG and knowledge systems

Search and Q&A grounded in your content, using hybrid retrieval, re-ranking, and graph-based methods that go well beyond basic vector search.

  • Hybrid search and re-rankingKeyword and vector retrieval combined, then re-ranked for precision.
  • GraphRAG and agentic retrievalKnowledge graphs and multi-step retrieval for questions that span many documents.
  • Permission-aware retrievalAnswers drawn only from content each user is allowed to see.
  • Citations and grounding checksEvery answer linked to its sources, with checks for unsupported claims.
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AI evaluation and observability

The difference between a demo and a dependable system. We measure quality on your real tasks, gate every release on it, and trace every agent step in production.

  • Custom eval suitesTask-specific test sets, LLM-as-judge scoring, and human review where it matters.
  • Model and prompt benchmarkingModels, prompts, and retrieval settings compared on accuracy, latency, and cost.
  • Evals in CI/CDRegression checks that block a release when quality drops.
  • Tracing and live monitoringOpenTelemetry traces of every agent step, with drift, cost, and quality alerts.
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AI governance, security and guardrails

Security and compliance designed for agents that take actions, aligned with the OWASP Top 10 for LLM applications, the NIST AI RMF, and the EU AI Act.

  • Red-teamingPrompt injection, jailbreak, data leakage, and tool-misuse testing.
  • Runtime guardrailsInput and output filters, PII redaction, and policy checks on every call.
  • Least-privilege agent accessScoped, time-limited permissions for every tool an agent can use.
  • Audit trailsA record of every prompt, decision, and action, mapped to your compliance needs.
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Model customization

When off-the-shelf models fall short, we adapt them to your domain or distill smaller models that run faster and cheaper.

  • Fine-tuningLoRA and full fine-tunes on open-weight and hosted models.
  • Distillation and small modelsCompact models that match large-model quality on narrow tasks.
  • Preference tuningOutputs aligned to your standards using feedback data.
  • Private deploymentModels run in your cloud or on-premise for full data control.
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Vision and document AI

Multimodal models that read documents and images the way people do, replacing brittle templates and manual data entry.

  • Intelligent document processingFields and tables extracted from invoices, forms, and contracts.
  • Multimodal understandingCharts, handwriting, photos, and diagrams read by vision-language models.
  • Classification and routingDocuments sorted and sent to the right workflow automatically.
  • Confidence-based reviewUncertain results routed to a person for a quick check.
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AI strategy and PoC-to-production

A clear path from AI ideas to systems in production, with value and risk checked at every stage.

  • AI readiness assessmentData, systems, skills, and governance reviewed against your goals.
  • Use-case prioritizationOpportunities ranked by value, feasibility, and risk.
  • Rapid proof of conceptA working prototype on your own data in weeks.
  • Production hardeningSecurity, scale, monitoring, and cost controls for launch.
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Enter where your team needs help.

Some projects begin with an opportunity to evaluate. Others start with a defined product build or an AI system that needs to work more reliably. We scope the work around the problem, the people who will use the result, and the systems it needs to fit.

  1. Understand the work

    Align on the workflow, users, constraints, available data, and what success would look like for this engagement.

  2. Design and build

    Bring the appropriate product, AI, data, and engineering specialists together to make and test the solution.

  3. Deploy and improve

    Prepare the system for use, document how it works, observe its performance, and plan the next improvements with your team.

Bring us the workflow or system you are working on.

Tell us what you want to build, what already exists, and where your team needs specialist help. We will start with the problem and identify a useful scope together.

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