Services Solutions · Neuro-Symbolic Middleware for Operations Research

Predictim GenOR-Twin

Predictim GenOR-Twin is an enterprise AI-powered operations research platform, available as both SaaS and developer API, that bridges natural-language operational reality and mathematical planning. Built on neuro-symbolic research validated across six NP-hard optimisation domains — job shop scheduling, vehicle routing, project scheduling, nurse scheduling, bin packing, and network flow — it reads unstructured shift logs, maintenance notes, and handover reports, and turns them into solver-ready constraints, with every extraction checked against a Knowledge Graph before it can touch a live schedule.

Operations research network graph showing optimized workflow nodes and scheduling connections

OR / AI · SEMANTIC MIDDLEWARE

Semantic alignment accuracy

99.7%

Hallucination catch rate

88.2%

Decision latency

15 min → 2 min

Illustrative results from representative deployments; not a guarantee of outcome.

Capabilities

What this platform does

Each capability is a deployed module — not a feature on a roadmap. Engagements typically begin with a 4-week scoping exercise to confirm fit against your operational data and constraints.

Natural language operational constraint capture

Operational teams communicate in plain language notes, emails, and handover logs. GenOR-Twin parses them to automatically extract and structure mathematical optimization constraints.

Operations research optimization engine

Leverages exact solvers and meta-heuristics to dynamically solve Mixed-Integer Linear Programming (MILP), routing, and resource scheduling models based on live constraint updates.

Constraints validated before they count

A symbolic Knowledge Graph validator checks every AI-extracted constraint for hallucinated entities or invalid values before it can reach the solver — catching the majority of incorrect extractions that a raw language model alone would miss.

Confidence-based repair or re-optimization

An adaptive policy chooses between a fast local schedule repair, a full re-optimization, or escalation to human review, based on the system's confidence in the extracted constraint and the schedule's remaining slack.

Cross-domain operational scalability

Validated across six NP-hard problem classes — job shop scheduling, vehicle routing, project scheduling, nurse scheduling, bin packing, and network flow — using historical operational logs from a Tier-1 automotive manufacturing floor.