Provectus puts AI where the work gets stuck

A croissant has an inconvenient property: it refuses to wait for your forecasting department. Order too few and the customer finds an empty shelf. Order too many and yesterday’s optimism becomes today’s waste. At Blue Bottle Coffee, this small daily wager became a problem large enough to require a different system.

The company behind the forecast is Provectus, an AI systems integrator. You hire it to connect a business problem to data, software and a working process. That description sounds less glamorous than inventing the future. It is also easier to put in a purchasing order.

The croissant problem

Blue Bottle’s cafe leaders had been estimating pastry orders from sales, inventory and expected growth. A method that suited a few locations became harder to manage across a growing network. The failure arrived in ordinary forms: missed sales on one side, excess stock on the other. Scale changed the calculation.

Provectus and Blue Bottle built a predictive ordering system on AWS. Data preparation, training and forecasting ran through separate pipelines; forecasts reached cafe leaders through a dashboard. Engineers had their own environment for comparing and improving models. Managers could adjust the recommendations. The system made a suggestion without pretending to know every local circumstance.

The 2020 account reports an 8% improvement in pastry ordering. That is a pleasingly untheatrical number. It describes a repeated decision becoming a little better, many times over. The lesson is in the mechanics: give people a prediction where they already make the order, and give engineers a way to keep improving it. Read the Blue Bottle account.

The queue before the decision

Now move from the pastry counter to specialty insurance. At Convex, underwriting depended on somebody first reading a dense engineering report, sometimes a hundred pages long, and preparing a structured summary. An expert’s judgment was waiting for another expert’s reading time.

Provectus built a document workflow that extracts, segments, classifies and summarizes reports. Its published case describes source-page references, a reviewer interface and deployment inside Convex’s AWS environment. Edge cases return to subject-matter experts. The company reports that a hundred-page report can become a structured summary in ten minutes.

The expensive question is often hiding behind a very tedious task.

Our reading of the customer cases

There is an important distinction here. A faster summary is useful only if the underwriter can inspect it. Traceability gives the reviewer somewhere to go when an assertion looks odd. Keeping the expert in the loop is part of the design, along with the model. The reusable version of this work sits behind Provectus’s Submission Flow blueprint. Explore the Convex workflow.

Six weeks, then the harder questions

Mad Mobile supplies point-of-sale technology to hospitality businesses. Its support representatives had to hunt through documentation on subjects such as Wi-Fi, device configuration and payment settings before answering a customer. This is a familiar office tax: the answer exists, but finding it interrupts everything.

The engagement began with discovery. Provectus mapped business needs, datasets, risks and architecture, including preliminary total-cost-of-ownership estimates for AWS and Cohere services. It then built a customer-service agent using Amazon Bedrock and a conversational interface integrated with Salesforce. The team tested more than ten hypotheses during development.

Six weeks brought a working prototype in the development environment. Production required a further gate: real support-team testing and an agreed accuracy target. That sequence is worth borrowing. A deadline should name the thing delivered. A prototype can answer whether an approach deserves investment; it cannot settle every question about reliability in everyday use. Inside the Mad Mobile engagement.

A platform that earns its keep

Another customer, an unnamed multinational healthcare enterprise, faced a quieter difficulty. Different AI teams used different tools and deployment practices. Every new use case carried another round of infrastructure setup and onboarding. Progress accumulated its own overhead.

Provectus paired a shared MLOps platform with an actual application: next-purchase prediction for a business line. The pairing matters. A platform can look wonderfully coherent until a real workload touches it. Here the reference application exercised the foundation, giving the team something concrete to build against.

The deliverables included a reusable project framework, a reference project and practical documentation. Other teams could begin from the template. This is how infrastructure becomes a business service: it reduces the effort of the next project, and somebody inside the customer can maintain it. See the healthcare platform case.

Five blueprints, with the keys included

Provectus’s 2026 catalog puts that repetition into five named blueprints. Submission Flow addresses underwriting triage. Portfolio Lens brings portfolio signals into underwriting. Asset Flow supports NAV pack preparation. Revenue Flow covers healthcare revenue-cycle operations. Evidence Lens joins patient-level evidence for healthcare and life sciences.

A blueprint is a starting system to tune for a customer’s policies, data and reviewers. The catalog promises working baselines, audit trails, cost controls and complete transfer of code and architecture. Browse the five blueprints.

This places Provectus in the market between specialist AI engineering, systems integration and industry consulting. Buyers can also choose a large integrator, buy industry software, or build internally. The useful comparison concerns who learns the workflow, who integrates the system, and who can operate it afterward.

Its current business model emphasizes milestone contracts and verified outcomes. That makes acceptance criteria consequential: the buyer and builder must agree what success means. Its origin in distributed data systems, followed by cloud and machine learning work, helps explain the emphasis on foundations. Co-founder Stepan Pushkarev now serves as CEO and CTO; fellow co-founders Gennady Galanter and Nick Antonov sit on the board. Meet the company and its delivery model.

Partnerships supply another part of the route to market. AWS and Provectus announced a multi-year collaboration in June 2025. In January 2026, Provectus joined Anthropic’s healthcare and life sciences launch ecosystem; June brought Select Partner recognition. Those relationships help explain its cloud and model choices. They are credentials, while the customer workflow remains the test. AWS collaboration · Anthropic recognition.

The engineers brew their own

The careers site offers an amusing glimpse of internal life. Hops handles professional-services operations. Barley is an AI delivery assistant. Malt brings recommendations to people processes. Even the naming system has a supply chain.

Its engineering culture is described as Ж-shaped: domain depth joined to technical range, curiosity and ownership. This is the company’s own description, but the public tooling provides something more tangible to examine. AWOS, its open-source Agentic Workflow Operating System, organizes coding assistance around planning and implementation. Its GitHub portfolio also includes UI for Apache Kafka. You can inspect the work before booking the conversation. See the internal tools and culture · Inspect AWOS.

Before you buy, count the handoffs

The copyable idea is to follow one task through the business. Where does it wait? Which inputs does it need? Who corrects the answer? Measure that sequence before changing it. Then test the new system on representative work, including the awkward exceptions.

Budget the whole route: engineering, cloud use, integration, evaluation, review and maintenance. Mad Mobile’s discovery process explicitly included operating-cost estimates before the build. That is a useful buying habit. A cheaper model call means little if reviewing its output consumes the savings.

The approach depends on usable data, agreed measures and people who can own the result. A forecast loses value when managers cannot act on it. A report summary loses value when its evidence cannot be checked. An inherited platform loses value when nobody can change it. Provectus’s strongest proposition is the attention it gives these surrounding jobs. The croissant, after all, still has to arrive on the shelf.