AI systems designed, built and run for production.
We take AI from a named workflow to a monitored system: strategy and data readiness, agents and generative AI, retrieval over your knowledge, conversational channels, predictive models and the operations that keep them reliable.
Where most AI projects start.
The eight capabilities buyers ask us about most often. Every one of them ends in a system in production, not a slide deck.
The complete AI catalog.
Grouped by where they sit in the lifecycle: plan, build, interact, integrate and operate.
Plan
Build
- AI AgentsAgents that take approved actions across your tools, with permissions, review points and monitoring built in.
- Generative AI & LLM DevelopmentApplications built on large language models that are grounded in your data, tested against real cases and costed before launch.
- AI App DevelopmentComplete AI products — interface, model layer, application code and integrations — designed for the people who will use them every day.
- Machine Learning & Predictive AnalyticsForecasting, classification, anomaly detection and recommendation models trained on your data and evaluated against the decisions they support.
- Computer VisionImage and video models for inspection, counting, monitoring and visual document work, built for the conditions they will run in.
- Model Adaptation & Fine-tuningDecide when prompting, retrieval, fine-tuning or a custom model is justified — and do it with evaluation data that proves the choice.
Interact
- RAG & Enterprise Knowledge SystemsAnswers drawn from your own documents and data, with sources shown, permissions respected and content kept current.
- AI ChatbotsText chat assistants for customer support, internal help desks and product experiences, grounded in your knowledge and connected to your systems.
- AI Voice Agents & ReceptionistsPhone agents that answer calls, understand callers in natural speech, book, route and take messages — and transfer to a person when needed.
- NLP & Conversational AIThe language layer underneath chat, voice and document systems — understanding, extraction, classification, dialogue management and multilingual support.
Integrate
- AI IntegrationConnect AI models and assistants to your CRM, ERP, databases, knowledge stores and APIs — with identity, logging and fallbacks that production systems need.
- AI Workflow AutomationAdd AI judgment — reading, classifying, deciding within limits — to multi-step processes, with rules where rules work and people where judgment matters.
Discover, prepare, build, evaluate, run.
The same five stages apply whether the result is an agent, an assistant or a forecasting model.
- 01
Discover
Pick the workflow, agree the outcome and record today's baseline.
Use-case brief and baseline
- 02
Prepare
Get access to the data and systems, check quality and settle the architecture.
Data and architecture plan
- 03
Build
Implement the model layer, integrations and interface in short, reviewed iterations.
Working increments every sprint
- 04
Evaluate
Test against real tasks, compare with the baseline and decide what needs a person.
Evaluation report
- 05
Run
Release in stages, monitor quality and cost, and improve with your team.
Monitoring and runbook
Agent, copilot, chatbot or model?
Different problems need different kinds of AI. This is how we help buyers self-select before a first conversation.
| Approach | What it does | Best when | People's role |
|---|---|---|---|
| AI agent | Plans and takes approved actions across tools and systems | The task needs judgement and action, and the limits can be written down | Approve risky actions, review exceptions |
| Copilot | Drafts, summarizes or suggests inside someone's existing work | Every output needs a person's sign-off | Decide on every output |
| Chatbot | Answers questions in text from approved knowledge | Many people ask similar questions | Handle escalations |
| Voice agent | Handles spoken calls: answering, routing, scheduling | Phone volume is high and requests are predictable | Take over complex calls |
| Predictive model | Forecasts, classifies or scores from historical data | There is enough history and a clear decision to support | Act on the prediction |
| Conventional automation | Runs fixed rules between systems | The steps are known and the inputs are structured | Own the rules |
Three ways to start.
Most AI work begins with a short discovery sprint, then moves into a build with a clear first release.
Discovery sprint
For a use case that needs shaping before anyone commits to a build — scope, data, feasibility and a plan.
- Workshops with the people who do the work
- Data and system access review
- Feasibility prototype where useful
- Phased plan and fixed-price proposal
Build and launch
For a defined system with a clear first release — an agent, an assistant, an integration or a product.
- Cross-functional delivery team
- Releases to staging every two weeks
- Evaluation suite and monitoring
- Handover documentation and runbooks
Run and improve
For systems already in production that need monitoring, tuning and a team that keeps extending them.
- Monitoring of quality, cost and latency
- Regular evaluation runs
- A standing backlog of improvements
- Scale the team up or down each quarter
Questions about AI projects.
Practical answers on integration, data and control. If yours isn't here, ask us directly.
Ask a question →We choose per task and keep the choice swappable — commercial APIs, open-weight models or both. The decision weighs quality on your evaluation set, cost, latency and where your data is allowed to go.
Often, yes. Many systems can run in your cloud account with private model endpoints; some models are only available as managed APIs. See Enterprise & Private AI.
We build an evaluation set of real tasks with known good outcomes during discovery, measure against it before every release and monitor the same measures in production.
Designs include a fallback: low-confidence answers go to a person, actions above a threshold need approval, and every output is logged so mistakes can be traced and fixed.
You do — code, prompts, evaluation sets and configuration. See how we work.
Discuss an AI project with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.