AI Engineering
Cloud & Workplace
Software Engineering
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We build LLM-powered applications — knowledge assistants, AI search, document intelligence and content tools — grounded in your data, measured against agreed quality standards and deployed with the access controls your business needs.
Outcome: Answers and content your people can check and trust.
AI Engineering
Large language models are good with language, but they don't know your products, policies or customers — and they can state wrong answers confidently. Business value comes from connecting models to the right information, controlling who can see what, and measuring quality.
We design generative AI applications around those requirements: retrieval from approved sources, models chosen for the task, evaluation against real questions, and deployment with monitoring.
We'll also tell you when a simpler approach — better search, a rules engine or a well-designed form — would serve the need better.
Problems we solve
Policies, manuals and past work spread across file shares, wikis and inboxes where people can't find them.
AI responses with no sources, so nobody knows which ones to trust.
Sensitive information that can't be sent to a public AI service without controls.
A demo that works on twenty documents but not on twenty thousand, or not for every team.
Capabilities
We scope each engagement to the problem — not every project needs every capability.
Applications
From internal assistants to customer-facing features.
Retrieval & data
Retrieval designed around your content, its structure and how often it changes.
Quality
Quality is tested before launch and tracked after it.
Security & deployment
Deployment options that match your data requirements.
Use cases
Typical scenarios, to show the kind of work involved.
Staff ask questions in plain language and get answers with links to the exact policy section.
A help experience that answers from product documentation and hands off to support when it can't.
Key terms and fields extracted from documents, validated and sent to the systems that need them.
First drafts of summaries and reports assembled from approved sources, ready for expert review.
One search across SharePoint, Confluence, file shares and ticket history that respects existing permissions.
How we deliver
Confirm the problem, the users, the sources and the data rules.
Build a working version on a representative sample of your content.
Measure accuracy, grounding and failure modes against real questions.
Add access control, scale, cost controls and monitoring.
Release to users in stages, with feedback built into the interface.
Track quality and cost in production and keep improving the test set.
Technology
Chosen to fit your environment and team, not a preferred vendor.
Built for production
We agree which data may be processed by which model, and keep sensitive workloads on private endpoints or self-hosted models.
Users only retrieve content they are already allowed to see; the AI layer never widens access.
Answers cite their sources, and the system says when it doesn't know rather than guessing.
Caching, model routing and token budgets keep running costs predictable as usage grows.
Engagement options
We'll recommend a model after the first conversation.
A defined scope and budget for a clear deliverable — ideal for builds, migrations and assessments.
Flexible capacity, invoiced monthly, for work where the scope will evolve as you learn.
A full-time pod embedded with your team for long-running products and platforms.
Fees tied to the KPIs we agree up front, where the result can be measured cleanly.
Client work
Designed and built an AI-assisted publishing platform with content automation, SEO tooling and built-in monetization.
Affiliated company
RAG retrieves relevant passages from your own content and gives them to the model with the question, so answers are based on your information rather than the model's general training. It also lets the application cite its sources.
Yes. Options include enterprise model endpoints in your own cloud tenant, such as Azure OpenAI or AWS Bedrock, and open-source models hosted on your infrastructure. The right choice depends on your data, quality needs and budget.
By grounding answers in retrieved sources, requiring citations, constraining answers to what the sources support, and testing against question sets that include cases where the right answer is “I don't know”.
We compare candidate models on your own data and tasks, then weigh quality against latency, cost and data requirements. Many applications use more than one model for different steps.
With an evaluation set built from real questions and expected answers, scored automatically and by reviewers, plus user feedback and monitoring once the application is live.
Resources
In 2026, AI in VDI and workspaces is fundamentally reshaping how organizations deliver secure, productive remote work environments. As hybrid and distributed teams become the…
Related services
Tell us what information your teams or customers struggle to find or use. We'll suggest where generative AI can help and how to measure it.
