Agentic Platform
Production-grade AI agents — orchestration, tools, memory and guardrails wired into your systems of record.
Engineered as the foundation, not the afterthought.
We build agentic systems that do real work: planners and specialised agents coordinating over your APIs, databases and documents through typed tools and MCP servers. Every run is grounded by retrieval and memory, constrained by policy guardrails and human approvals, and measured with evaluation suites, tracing and per-token cost budgets — so autonomy ships to production without losing control.
Define agents. Give them work.
The Agentic workspace is where teams declare agents and the tasks they own. Each agent carries its own skills, step-by-step instructions, hooks and typed tools, backed by a shared library, teams and analytics — so autonomy is versioned, reviewable and measurable instead of buried in prompts.
- Agents with scoped tasks, memory and tools
- Skills and instructions authored as markdown
- Hooks to trigger runs from your systems
- Shared library, teams and role-based access
- Run analytics, tracing and cost visibility
- Multi-model brains — OpenAI, Gemini, NVIDIA
Agents wired to data products and ML features.
Agents call data products and feature pipelines as first-class tools: retrieval over your lakehouse, ML forecasting and scoring models, structured parsers and memory in Postgres, and webhook workflows that orchestrate the whole graph. Every run returns typed, renderable output — not free text.
- Data products exposed as typed agent tools
- ML forecasting and scoring on demand
- Structured parsers and schema-validated output
- Postgres chat memory and persisted runs
- Webhook-triggered multi-agent workflows
- Real-world tools — search, weather, financial
Molecule search, ADME and toxicity forecasting.
Send a SMILES string and the BioMed agent returns a full molecule report: solubility and pharmacokinetics, drug-likeness rules, ADMET-AI toxicity endpoints scored against DrugBank percentiles, BOILED-Egg permeation, target predictions and an interactive 3D conformer. The same pattern applies to any domain where an ML feature store meets an agent.
Solar and wind farms, read by agents.
Energy agents work your renewable portfolio directly: ask about a wind or solar asset and get the ISO pricing node analysed end to end — day-ahead LMP previews, mean and median $/MWh, day-by-hour heatmaps, distribution and cumulative curves, and the top and bottom settlement intervals. The same agent compiles structured asset briefs — location, capacity, operator, economic and environmental impact — so insight arrives decision-ready instead of as raw market data.
- Day-ahead LMP previews with flagged peaks
- Day-by-hour price heatmaps per pricing node
- Distribution, percentile and cumulative analysis
- Top / bottom settlement intervals for bid strategy
- Structured asset briefs with sourced facts
- Hooks for alerts, scheduled runs and reporting
