Services
AI services, and the engineering underneath
Six ways we put AI to work, and the platform, cloud and interface work that keeps an AI system running once it meets real users.
AI services
Each one is backed by something we have shipped, linked from the page.
AI Agents & Multi-Agent Systems
Role-based agents that act on real systems: tool calling, memory, structured replies and human approval where it matters. Built on Microsoft Agent Framework, LangChain and OpenAI-compatible models.
- Single agents and role-based multi-agent teams
- Tool calling against your real systems, with approval gates
- Grounded replies: the server shows only what a tool returned
MCP Servers & AI Integrations
Put your product or data in front of ChatGPT, Claude and other AI hosts through the Model Context Protocol. Secured with OAuth 2.1 and tested against each host's rules.
- MCP servers for search, data and actions in your product
- OAuth 2.1 with Entra, APIM or your identity provider
- Conformance with the rules of each AI host
RAG & Knowledge Systems
Retrieval built for your material, not generic chunking: hybrid search, GraphRAG, code-aware indexing and incremental refresh on pgvector, Azure AI Search or Neo4j.
- Hybrid retrieval: semantic, lexical and graph signals combined
- Chunking that follows the structure of the material
- Incremental re-indexing keyed to what changed
AI-Native Product Development
A product designed around the model from day one, with the platform engineering to run it: multi-tenancy, queues, observability and cost control.
- From idea to working prototype in weeks
- Agent, API, web app and data pipelines from one team
- Multi-tenant, observable and cost-controlled from the start
AI Strategy & Readiness
A short, hands-on assessment of where AI pays off in your operation, what data and guardrails you need, and a build plan with honest estimates.
- One to two weeks, fixed price
- Opportunity map ranked by value, risk and data readiness
- Architecture options with monthly running costs
Applied Machine Learning & Forecasting
Forecasting, scoring, clustering and anomaly detection on your own data, in Python on PyTorch and TensorFlow, with the data pipelines that make the models trustworthy.
- Time-series forecasting and demand models
- Scoring, segmentation and anomaly detection
- ETL and a modelled database before any model
The engineering underneath
Most of what an AI system does is call ordinary software. We build that too, and we build it to last.
Custom Software Development
Platforms, back offices and integrations built to run for years: .NET and Python services, queues, payments, real-time delivery, and the APIs an AI system will need.
Learn moreCloud & DevOps
Azure and AWS infrastructure for AI and platform workloads: identity, API management, observability, CI/CD and cost control, as code.
Learn moreIoT & Connected Systems
Devices, radios and the cloud that receives their data: BLE, LoRa and LTE-M hardware, MQTT ingestion, real-time dashboards and the analytics on top.
Learn moreWeb & Mobile Development
Fast, accessible web apps and mobile-first PWAs in Blazor, SvelteKit and React, designed with the people who will use them.
Learn moreNot sure which one you need?
Start with a short AI readiness assessment, or just tell us the problem and we will say which of these fits.