Operational Intelligence from context to outcome.
ConstraintFlow connects enterprise data, domain knowledge, AI, optimization and governed workflows into a shared intelligence layer for complex operational decisions.
Most enterprise AI stops at the answer. ConstraintFlow follows the decision through the outcome.
- Question
- Context
- Decision
- Action
- Outcome
- Learning
Question → Answer
Context → Decision → Action → Outcome → Learning
From fragmented context to governed decisions, action and learning.
Read top to bottom. Enterprise and domain context is connected and governed, modelled as a living representation of the operation, evaluated by Decision Intelligence and turned into action through governed workflows — with people accountable at the decision point and outcomes feeding the next cycle.
Enterprise + Domain Systems
Systems of record, operational and scientific systems, data platforms, documents, events and the people who hold the knowledge.
- Zahir · Data & Context Plane
Connect and govern the context
Brings fragmented data and domain context together into governed, reusable information models — integration, entity relationships, current and historical state, quality, lineage and semantic access.
Explore → - Living Context Model
A continuously updated model of the operation
The entities, relationships, constraints, evidence and state a decision depends on. In Manufacturing this is the Operational Digital Twin; in Life Sciences, the Scientific Context Model.
Explore → - Decision Intelligence
Evaluate options against real constraints
Combines deterministic rules, optimization, machine learning, retrieval, simulation and AI reasoning according to the decision being made. Not every decision is a language-model call.
Explore → - Gurita · Agent & Workflow Plane
Coordinate agents, tools and workflows
Turns recommendations into governed workflows — agents, enterprise tools, approvals, escalations, notifications and human handoffs, with policy enforcement and auditability throughout.
Explore → Human Decision
The people accountable for the outcome review the recommendation, evidence and trade-offs — then approve, adjust or reject.
Explore →Action
Advisory, approved or controlled automation — the execution boundary is configured per decision, never assumed.
Outcome
What actually happened: execution, variance against the plan and the measurable result.
- Learning Plane
Learning
Outcomes, feedback and approved decisions refine knowledge, rules, models and workflows — measured, governed improvement, not silent self-modification.
Explore →
Connect the systems that already run your organization
ConstraintFlow sits above existing systems rather than requiring organizations to replace them.
ERP • MES • WMS • Machines • Planning • Supply Chain
Life SciencesScientific datasets • Publications • Experimental systems • Research knowledge • External evidence
Start with the systems and context required for the first decision, then expand the operational model over time.
Understand → Decide → Act → Measure → Improve
Five horizontal capabilities. Every industry implementation runs the same cycle on the same foundation.
Understand
Create governed context from fragmented systems, data, documents, events and domain knowledge.
Decide
Apply the appropriate combination of rules, optimization, machine learning, retrieval, simulation and AI reasoning.
Act
Coordinate people, systems and AI agents through governed workflows.
Measure
Connect decisions and actions to observable outcomes.
Improve
Use outcomes, feedback and approved decisions to improve future recommendations and workflows.
One platform. Industry-deep intelligence.
ConstraintFlow uses the same intelligence architecture across industries while allowing each industry to maintain its own domain model, constraints, workflows, evidence and decision logic.
Manufacturing
Operational Digital Twin
Model machines, orders, materials, capacity, labor, constraints, customers and operational dependencies as a connected representation of the operation.
Explore Manufacturing →
Life Sciences
Scientific Context Model
Connect genes, proteins, targets, diseases, variants, experiments, datasets, publications, evidence and hypotheses into governed scientific context.
Explore Life Sciences →
Different industries. Same fundamental problem: turning fragmented context into better decisions.
Start with one difficult decision.
You do not need to transform the entire enterprise before creating value. Start with a high-value decision, connect the context required to make it better, measure the outcome and expand from there.