Decision copilots
Recommendations with rationale, delivered where the decision happens.
Services
Decision Intelligence
Combine data, models, and workflows so leaders and operators decide with clarity under constraints.
- 25%better decisions on measured KPIs
- 100%recommendations with visible rationale
- Weeksfrom question to decision system
Capabilities
From dashboards to decisions
AI-powered insights and decision support for faster, better enterprise decisions.
Scenario analysis
What-if modeling that turns assumptions into comparable outcomes.
Operational recommendations
Pricing, allocation, scheduling and routing decisions optimized continuously.
Feedback and learning loops
Decisions tracked against outcomes so the system improves with use.
How we deliver
Recommendations people can interrogate
Decision systems earn trust through transparency: every recommendation shows its drivers, its confidence and its trade-offs.
Modeling
Your decision, formalized honestly
We work with your experts to encode objectives, constraints and trade-offs explicitly — combining optimization, ML and LLM reasoning where each is strongest.
- Objectives and constraints made explicit with owners
- Optimization + ML + LLM reasoning, by fit
- Assumptions documented and challengeable
Shared stateHandoffsRetriesTraces
Delivery
In the flow, with the why
Recommendations arrive inside the planning tool, CRM or ops console — with driver attribution and plain-language rationale a decision-maker can question and override.
- Embedded in existing tools, not another dashboard
- Driver attribution behind every recommendation
- Overrides captured with reasons, feeding learning
Copilot
Draft renewal summary for Acme Corp
3 accounts show churn risk — view analysis
Ask anything…
Learning
Closed loops that compound
Decisions and outcomes are tracked together; the system learns from what actually happened — and shows you where its recommendations beat the baseline.
- Outcome tracking against recommendations
- Champion/challenger evaluation in production
- Quarterly lift reporting against your baseline
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Use cases
Decisions we've improved
Pricing decisions
Guardrailed recommendations with margin trade-offs visible.
Inventory allocation
Stock positioned against demand and service targets.
Workforce scheduling
Coverage optimized against demand and preferences.
Credit and risk calls
Consistent, explainable decision support.
Marketing spend
Budget shifted to measured incremental impact.
Maintenance prioritization
Limited crews aimed at highest-risk assets.
Delivery
How we engage
Flexible models — from strategy sprints to dedicated pods — with clear phases, quality gates, and ownership through production.
- Clear delivery process
- Senior engineering talent
- Security built in
- Long-term capability transfer
-
1
Align
Goals, constraints, success metrics and stakeholders.
-
2
Design
Architecture, delivery plan and risk controls.
-
3
Deliver
Build, integrate, harden and launch.
-
4
Operate
Monitor, optimize cost and continuously improve.
The first month, our planners overrode half the recommendations. The system learned from every override — by quarter's end they were accepting 80%, and service levels proved them right.
Works with your stack
Analytics stack, upgraded
Built on your warehouse and BI investments, adding the decision layer they're missing.
- Snowflake
- Databricks
- BigQuery
- dbt
- Python
- OR-Tools
- Anthropic Claude
- OpenAI
- Power BI
- Looker
- Kafka
- Kubernetes
FAQ
Common questions
How is this different from our BI dashboards?
Dashboards describe; decision intelligence prescribes. Instead of charts someone must interpret, the system recommends an action with rationale — and learns from whether it was taken and how it turned out.
Will people trust algorithmic recommendations?
They trust what they can interrogate. Visible drivers, honest confidence, easy overrides and a track record — trust is designed, then earned with results.
Do we need perfect data first?
No — we start with decisions where existing data carries signal, are explicit about uncertainty, and let the value case fund data improvements.
Optimization, ML or LLMs — which do you use?
Whichever fits: optimization for constrained allocation, ML for prediction, LLMs for reasoning over unstructured context and explanation. Most real systems combine all three.
Next step
Upgrade your highest-leverage decisions
Pick one recurring decision with real money attached — we'll build the system that makes it better.