AI models
The best-fit model for each job, switched as better ones appear.
Tools we use
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Hugging Face
- Azure AI Foundry
- Amazon Bedrock
- Google Vertex AI
Service
Full-stack AI solutions.
AI models, apps and agents, and the integration and cloud work around them, built in your environment and handed over to your team.
Overview
What it is.
Whether you are building an AI product or changing how an enterprise runs, we build solutions that work in the real world, not just in labs. We pick models and tools to fit your cloud, your data and your budget.
Who we work with.
Large enterprises
Production-ready, secure AI products and platforms, built alongside your teams.
Funded startups
Your extended AI product team, from first design to launch.
Capabilities
What we build.
AI apps and agents
Assistants and agents that search your data, follow your rules and act in your systems.
Tools we use
- LangGraph
- LlamaIndex
- CrewAI
- n8n
- pgvector
- OpenSearch
- evals
Full-stack apps and integration
Web and mobile apps, APIs and connectors that put AI in front of your users.
Tools we use
- Python
- React
- Next.js
- Node.js
- Flutter
- Java
- REST APIs
- MCP connectors
Cloud, DevOps and security
Automated build, test and deployment on any major cloud or on-premise.
Tools we use
- AWS
- Azure
- Google Cloud
- Kubernetes
- Docker
- Terraform
- GitLab CI/CD
- HashiCorp Vault
- Prometheus
- Grafana
Built into every project
- access control
- data privacy and PII masking
- audit trails
- cost, error and quality monitoring
- data kept in your region
How we engage
Start with one workflow. Scale on outcomes.
A fixed-scope pilot, run by a pod inside your environment and measured against your own baseline.
Step 1
Discover
We sit with the people who run the process, map the SOP, data and controls, and pick one high-volume workflow.
- SOP
- data
- controls
In this servicePick the user journey and the systems it touches.
Step 2 · fixed scope
6–8 week pilot
A forward-deployed pod builds it in your environment and measures it against your own baseline.
- FDE lead
- AI engineer
- Solution architect
In this serviceOne app or agent live in your cloud.
Step 3
Scale on outcomes
Expansion is priced on outcomes, such as tasks completed and hours saved, with no platform lock-in.
- handover
- runbooks
- your keys
In this serviceMore journeys on the same stack.
Typical engagement: 6–8 week pilot · one pod · one workflow live · measured against your own baseline
See how we engageAccelerators
Accelerators we bring.
TerraFlow
A governed execution layer for enterprise AI and agent workloads.
Explore TerraFlowIn an engagement
Policy decides where each workload may run, and every execution leaves an auditable route receipt, so the pilot is governed from its first request.
Clarion
Meeting intelligence that never leaves your network.
Explore ClarionIn an engagement
Transcription, decisions and action items from Teams, Zoom and Google Meet, synced into Salesforce and Jira, fully on-premise.
Sample use cases.
Sample use case
Private assistants over your documents
Staff can ask questions across your own documents and data and get grounded answers, with access rules respected.
Sample use case
Meeting-to-CRM automation
Decisions and action items from meetings can be written into Salesforce and Jira, with recordings kept on your own infrastructure.
Sample use case
Governed document extraction
Fields can be extracted from documents with a reviewer in the loop and every step recorded in an audit trail.
Where it applies
Industries and a solution story.
Solution story
An academic lifecycle platform with AI answer-sheet grading
Attendance, fees, exams and grading lived in disconnected systems; students, faculty and parents chased information across platforms while handwritten answer sheets waited to be graded.
Applies to
- Schools
- Colleges
- Universities
Let's build it together.
Bring one workflow. Leave with a governed system running in your environment.