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AI & Data Innovation

AI development services

We find the workflows where AI pays for itself, build the data foundations under them, and ship models and agents your teams can trust in production.

01Overview

Most AI projects stall between the demo and the workflow.

A prototype that answers questions in a chat window is easy to build. A system that reads your contracts, updates your CRM and knows when to hand off to a person is a different piece of engineering.

We treat AI as software. Each system is scoped to a measurable job, connected to the systems that already hold your data, tested like everything else we ship and monitored once it's live.

03Outcomes

What changes for your business

  • 01

    Fewer manual hand-offs

    Agents and automations take over the repetitive steps between systems, so people spend their time on judgment calls.

  • 02

    Decisions on current data

    Pipelines and dashboards that refresh on their own, instead of spreadsheets rebuilt every Monday.

  • 03

    AI you can audit

    Every model call logged, every agent action traceable, every prompt versioned.

  • 04

    Costs that stay predictable

    Model routing, caching and usage limits designed in from the first sprint.

04Approach

How we deliver

  1. 01

    Discover

    Map workflows, data sources and constraints. Rank use cases by value and feasibility.

  2. 02

    Validate

    Build a working proof on real data in two to four weeks and measure it against the baseline.

  3. 03

    Build

    Engineer the production version: integrations, guardrails, evaluation suites and monitoring.

  4. 04

    Scale

    Roll out to more teams and workflows, with governance and cost controls already in place.

05Stack

Tools we use most here

  • Claude
  • Gemini
  • Python
  • pandas
  • Snowflake
  • PostgreSQL
  • OpenAI
  • LlamaIndex
  • Pinecone
  • Weaviate
  • Ollama
  • MLflow
  • LangChain
  • n8n
  • Qdrant
  • FastAPI
  • PyTorch
  • TensorFlow

07FAQ

Frequently asked questions

View pricing guide
Do we need clean data before starting?

No. Most discovery work starts with messy data. We assess what you have, fix what blocks the first use case and improve the rest as the system grows.

Which models do you work with?

Claude, Gemini, Mistral, open-weight models such as Llama, and classical machine learning where it fits better. We choose per use case and keep the architecture model-agnostic so you can switch later.

How long until something is live?

A focused use case usually reaches production in eight to twelve weeks: two to four weeks of validation, then the build.

How do you keep sensitive data safe?

Data stays in your cloud where possible, personal data is masked before it reaches a model, and access follows your existing roles. Every data flow is documented for your security team.

AI & Data Innovation

Tell us what you're trying to change

A senior architect from this practice will reply within one business day with questions, ideas and a suggested first step.