Optiflux Technologies Private Limited builds mathematical optimization, forecasting, and GenAI systems for enterprises that run supply chains, plants, and stores, turning operational data into schedules, stock levels, and plans that measurably lower cost and lift service.
Design the right network, place the right stock. Multi-echelon inventory, network design, and allocation models that cut holding cost without breaking service levels.
Master production schedules and shop-floor sequences that minimize changeovers, balance line loads, and hit delivery dates, built on MILP, constraint programming, and metaheuristics.
ML forecasts at SKU–location granularity that read seasonality, promotions, and external signals, feeding replenishment and S&OP directly.
Sensor and process data turned into failure predictions and quality drift alerts: fewer unplanned stops, tighter first-pass yield.
Assortment, pricing, and store performance analytics for retail chains, from category insights to store-level replenishment logic.
Production-grade GenAI: document intelligence, decision copilots, and workflow automation grounded in your enterprise data, not demos.
We map your operating environment, constraints, and objectives through data analysis and stakeholder interviews.
Specialists build custom mathematical models that capture the real complexity of your processes.
Linear & integer programming, constraint programming, and metaheuristics tuned to your problem.
Solutions plug into your existing systems and workflows for seamless adoption.
Models are continuously improved against feedback and changing business conditions.
Production planning, scheduling, and predictive maintenance for plants and multi-site manufacturing networks.
Fleet dispatch, blend optimisation, and mine-to-port logistics for mining and metals operations.
Fleet assignment, crew pairing and rostering, maintenance routing, and disruption recovery for airlines.
Vehicle routing, load and container planning, and network design for logistics and distribution operations.
Assortment, pricing, and replenishment optimization for retail chains and consumer goods businesses.
Risk analytics, cash and network optimization, and explainable forecasting for banks and financial institutions.
The Optiflux Loop is how an optimization model gets from your constraints to your P&L, and keeps earning after it ships. Every stage produces something you can check: a quantified baseline, a backtested model, a running system, and the drift report that sends it round again.
Map constraints, objectives, and data
Quantify what today's plan costs
Formulate the decision mathematically
Solve it: MILP, CP, metaheuristics
Backtest against your own baseline
Land it in the systems you run
Track decision quality and drift
Retune as cost and capacity move
Solver choice follows the model, not habit. Most mixed-integer work runs on Gurobi or HiGHS; the rest earn their place on particular structures: conic programs, huge LPs, scheduling problems that a constraint solver closes in seconds and a MILP will not.
Commercial engines where the problem justifies the licence, open ones where it does not.
Where the problem gets expressed. Scheduling and rostering often close faster as constraint programs than as MILPs, so both stay on the table.
Demand, price, and failure models built to feed the optimisation rather than sit beside it in a dashboard.
For the questions a deterministic model answers badly: queueing, variability, and what a plan costs when reality does not cooperate.
What turns a model into something that runs on a schedule against live data, instead of a notebook someone re-runs by hand.
Retrieval and agent systems grounded in enterprise data, with evaluation harnesses so accuracy is measured rather than assumed.
Optiflux builds mathematical optimization, forecasting, and GenAI systems for enterprise operations. We turn planning decisions, such as network design, production schedules, inventory targets, demand forecasts, into models that run in production and feed your existing systems, rather than into slide decks.
Business intelligence describes what happened; optimization prescribes what to do next. A BI dashboard reports that service levels fell; an optimization model computes the inventory placement, production sequence, or allocation plan that raises them at the lowest cost, subject to your real capacity and lead-time constraints.
Manufacturing, mining and metals, airlines, logistics, retail and CPG, and banking and financial services, each with a dedicated practice. The common thread is operations with enough scale and constraint complexity that spreadsheet planning leaves measurable money on the table: multi-site plants, haul fleets, crew rosters, and multi-echelon distribution networks.
Eight stages that close into a loop: discover, baseline, model, optimize, validate, integrate, monitor, refine. We map your constraints, quantify what today's decisions cost, build and solve the model, backtest it against that baseline, land it in the systems your team already uses, then watch for drift and retune, which feeds straight back into discovery.
A focused engagement typically reaches a working model in weeks rather than quarters, because we scope to one decision, whether a scheduling problem, a replenishment policy, a forecast, instead of a platform. Integration timelines then depend on your systems, not on the modelling.
Mixed-integer linear programming, constraint programming, network flow models, and metaheuristics for optimization; gradient boosting, hierarchical reconciliation, and probabilistic methods for forecasting; and retrieval-augmented generation with evaluation harnesses for GenAI systems.
No. Data quality work is part of the engagement. Most operational data is incomplete, inconsistent, or spread across systems, and models are built to tolerate that. We quantify what the gaps cost in accuracy rather than waiting for a data programme to finish first.
Optiflux Technologies Private Limited is headquartered in HSR Layout, Bangalore, Karnataka, India, and serves clients across India, USA, UK, Middle East, Australia and Canada. The founding team came out of IIT Kharagpur, with prior operations-research work at Optym and DecisionOpt.
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