Your operations
have optimal
parameters.
We build a simulator from your historical data, deploy an autonomous AI agent, and run experiments until the answer is found. Done for you. Delivered in a week.
Five domains.
One pattern.
The autoresearch pattern is domain-agnostic. Anywhere you have a measurable objective, historical data, and controllable parameters, the same approach applies.
Cost reduction · V3 experiment
Reorder points, safety stock, and order quantities optimised against your actual demand patterns and cost structure. Full perishable goods support with FIFO batch tracking, shelf-life-aware ordering, and waste-vs-stockout trade-off analysis.
Full experiment report — 15,435 simulations →Profit improvement · V4 experiment
Price elasticity modelling and optimal price point discovery across your product catalogue. We fit demand curves from historical sales data, then let the agent find prices that maximise revenue or margin — including bundle and loss-leader dynamics.
Full experiment report — 35,534 simulations →Diminishing returns modelling per channel with optimised budget splits. We replace platform-reported ROAS with independently verified allocation across Google, Meta, and beyond.
Shift staffing matched to demand patterns, minimising labour cost while meeting service level targets. Built for restaurants, retail, call centres, and any business with variable-demand shift work.
Route sequence optimisation for delivery companies, field service, and last-mile operators. Minimise total distance, time, and fuel cost while respecting vehicle capacity and delivery time windows.
Done for you.
Delivered in a week.
A consulting engagement. You share your data. We scope the problem, build the simulator, run the autonomous optimisation loop, and deliver actionable parameter recommendations. No new software required.
Sales history, costs, inventory levels, pricing records, schedules, or routes — whatever the domain requires. CSV, Excel, or direct export from your existing system.
We define the objective metric and construct a deterministic model of your operations from your historical data. This is the foundation the agent runs experiments against.
The autonomous AI agent adjusts parameters, measures results, keeps what works, and reverts what doesn't. 30 to 15,000 experiments depending on problem complexity.
One person.
Full accountability.
Four years in global market research — structured intelligence models, data-driven analysis across technology, manufacturing, healthcare, and financial services. Then the same rigour applied to operational optimisation.
Every engagement is scoped, built, and delivered personally. No handoffs, no junior analysts, no outsourcing. You work directly with the person who built the system.
Connect on LinkedIn →Real experiments.
Real results.
The same autonomous optimisation pattern validated across two domains at escalating levels of complexity. Each version is harder. Each version proves the pattern holds.
5 products, fixed lead times, simple stockout and holding cost formula. The proof-of-concept that started the pattern.
12 products, per-product lead times, quantity discount tiers, 36-parameter search space. The agent discovered sharp cliff boundaries where a 1-unit change caused an 8× jump in missed sales.
12 products including perishables with FIFO batch tracking and variable shelf lives. Bayesian GP surrogate. Same improvement as V2, 77% fewer experiments, on a harder problem.
8 products with price elasticity and demand cliffs. The agent pushed every product to its exact cliff threshold — the highest price before demand collapses.
Cross-price elasticity across substitute groups, 3 product families. When premium_widget's price rose, demand shifted to standard_widget. The agent learned to optimise group-level dynamics.
Monthly pricing across 3 product bundles, 99 parameters. £578k → £1,027k. The agent discovered that pricing budget_widget at cost generates £482k in bundle revenue.
What others provide.
What we provide.
Most approaches to operational improvement rely on guesswork, generic benchmarks, or tools that require you to do the work. This is different.
Overcome hesitations before they block conversions.
Every engagement is reviewed personally. If the pattern doesn't apply clearly in the scoping call, we say so and don't take the engagement.
Historical operational data — sales history, costs, inventory levels, pricing records, schedules, or routes depending on the domain. Minimum 3–6 months. CSV, Excel, or direct export from your existing system.
5–7 working days from the moment we receive your data. Total timeline from first contact to delivered recommendations: typically 2–3 weeks including the scoping call.
We scope before we commit. If the pattern doesn't apply clearly in the scoping call, we say so and don't take the engagement. We don't take projects we don't expect to deliver meaningful results on.
No. You receive a recommendations document with optimal parameter values, a full experiment log, and structural findings. You implement the parameters in whatever tools you already use — ERP, spreadsheet, Shopify, or otherwise.
Client data is encrypted at rest, transmitted over HTTPS only, and deleted from all systems within 90 days of delivery. We act as a data processor under a signed DPA. No data is shared with third parties.
Start with
one problem.
Share your data. Define the metric. We scope the engagement in one call and tell you exactly whether the pattern applies and by how much.