Data strategy and pipelines
A review of your data setup and a clear plan, with automated data feeds, scheduled or real time.
Service
From data to agentic AI.
From scattered data to AI agents that do real work, on your platform, in your environment.
Overview
What it is.
We get your data ready first, then put AI to work on it: clean data becomes trusted insights, then AI that answers, then AI that acts.
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.
Get your data ready
Data platform and governance
One organised store for all your data, with access rules, personal-data masking and a clear record of where data came from.
Analytics and dashboards
Dashboards leaders can trust, with one agreed definition for every key number.
Put AI to work
AI that knows your business
Search across your own documents and data, with answers that cite their sources and respect who is allowed to see what.
Agents and multi-agent workflows
Agents built from your SOPs that take action in your systems, with human hand-off and governed MCP connectors.
AI you can trust at scale
AI answers tested and graded, with guardrails, audit trails, and cost and speed tracking.
- Clean data
- Trusted insights
- AI that answers
- AI that acts
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 serviceReview your data setup and agree one definition for each key number.
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 governed pipeline feeding one agent.
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 sources, more agents.
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.
Sales Intelligence
Market signals and your own conversations, turned into qualified pipeline.
Explore Sales IntelligenceIn an engagement
MCA, GST, tender, funding, hiring and regional-language news signals, scored against your ICP and pushed into your CRM with an explanation.
Sample use cases.
Sample use case
RAG-powered fraud and risk investigation
Investigators can ask questions across case files, transactions and policy documents and get grounded answers with the sources cited.
Sample use case
Grounded, cited answers for clinical and protocol queries
Clinical and operations staff can search protocols and regulated knowledge and receive answers that cite the source document.
Sample use case
Provider and code validation before claims
Provider identities and diagnosis codes can be validated and cross-checked so anomalies are flagged before a claim is submitted.
Where it applies
Industries and a solution story.
Solution story
Provider and diagnosis-code validation with MCP servers
A healthcare provider platform validated provider identities against the national registry and mapped diagnoses to ICD-10 codes by hand, causing claim errors, rework and compliance risk.
Applies to
- Payers
- Providers
- Revenue-cycle teams
Let's build it together.
Bring one workflow. Leave with a governed system running in your environment.