AI Development & Automation

AI inside the product you already have.Built to work in production, not just in a demo.

We add AI to existing .NET, Python and mobile products: assistants that answer from your own data, agents that complete real tasks, and automation that removes manual work. Every feature is measured, monitored and secured like the rest of your software.

See what we check

At a glance

First step
Discover · 1–2 weeks
Who does the work
Senior engineers, hands on
Ownership
Your code, your IP
Ways to engage
Project, team or support
  • You own all code and IP
  • NDA before we start
  • 24/7 support available
  • Microsoft certified engineers

Why it matters now

  • Your data is the advantage

    Every competitor can call the same models. What makes AI useful is connecting it to your documents, databases and workflows, safely.

  • Agents can now do the work

    With tool calling and the Model Context Protocol (MCP), AI can look things up, update records and hand off to people, not just write text.

  • Demos are easy; production is not

    Accuracy, cost, latency and security decide whether an AI feature survives contact with real users. That is engineering work.

What we build

The building blocks we use, chosen for each problem rather than applied by default.

  • RAGAnswers from your documentsSearch, chunking, embeddings and reranking, with sources cited
  • AGENTAI agents with tool callingMulti-step tasks across your APIs, with approval for sensitive actions
  • MCPModel Context ProtocolStandard, reusable connections between AI and your systems
  • SQLQuestions over your databasesNatural language to validated SQL, never unchecked queries
  • DOCSDocument processingExtraction and summaries from PDFs, forms and emails
  • ROUTEModel routing and cachingThe right model per task, lower cost and latency
  • EVALEvaluationTest sets that score accuracy on every model or prompt change
  • OBSObservabilityTraces, costs and quality tracked in production

Proof: related client work

Real engagements where we did this work, with the details.

How we work

  1. 011–2 weeks

    Discover

    Find the use cases where AI pays off, check your data, and agree how success will be measured.

  2. 024–6 weeks

    Pilot

    Build a working version on your real data, with evaluations from day one, and test it with real users.

  3. 03Per scope

    Integrate

    Bring it into your product properly: security, permissions, monitoring, and your release process.

  4. 04Ongoing

    Improve

    Track quality and cost in production, and improve prompts, retrieval and models as you learn.

What you get

  • A working AI feature in your product, not a separate demo
  • Evaluation test set and quality scores
  • Monitoring for quality, latency and cost
  • Security review and guardrails
  • Documentation your team can maintain
  • Source code and IP owned by you

Tools we use

  • Semantic Kernel
  • Microsoft Agent Framework
  • Pydantic AI
  • LangChain
  • MCP
  • pgvector
  • Qdrant
  • Azure AI Search
  • Arize Phoenix
  • OpenTelemetry

Platforms and models

Azure AI Foundry
Azure OpenAI and other models, with enterprise security and data controls.
Google Vertex AI
Gemini and open models on Google Cloud.
OCI Generative AI
Generative AI services on Oracle Cloud Infrastructure.
Models
GPT, Claude, Gemini, Llama and Mistral, chosen per task on quality, cost and data rules.

Ways to work with us

  • For a clear, defined goal

    Fixed-scope project

    Agreed scope, milestones and price before we start.

  • For ongoing product work

    Dedicated team

    Engineers who work as part of your team and scale with your roadmap.

  • For systems in production

    Support agreement

    A monthly block of hours for maintenance and improvements, with agreed response times.

Questions

Do we need to rebuild our product to add AI?

No. Most of our AI work adds features to existing .NET, Python and mobile applications through their current APIs and data.

Will our data be used to train AI models?

No. We use enterprise services such as Azure OpenAI, where your prompts and data are not used to train the models, and we only access what the feature needs.

How do you know the AI is giving good answers?

We build an evaluation set with you at the start and score every change against it, then keep measuring quality in production.

Find the AI feature worth building first.

In 30 minutes we can usually tell you where AI pays off in your product, and what a pilot would take.