Provectus

AI and ML

AI Infrastructure & Operations

Builds the infrastructure and controls for AI workloads spanning model providers, cloud resources, company data, and tools. Covers usage and cost attribution, access and identity governance, and reliability monitoring across the full request lifecycle.

Product engineering

AI Engineering

Builds the engineering harness around agents — task decomposition, scoped execution, tool integration, verification, and recovery. Helps teams delegate larger units of work and measures accepted results, intervention, rework, and delivery performance across the SDLC.

AI and ML

Enterprise AI Applications

Builds the systems behind enterprise AI applications: specialist agents, shared state, retrieval, and integrations with business tools. Orchestration brings parts together to solve complex tasks; evaluation and runtime infrastructure make work measurable and operable.

Advisory and strategy

Blueprint Activation

Five production-tested AI systems for FSI and HCLS activated on the client's data, systems, and team. Clients pick the blueprint closest to their business and Provectus deploys and tunes it.

Advisory and strategy

Legacy Modernization

A guided approach to modernizing systems the business already runs on. AI reads the existing system first; the client decides which layer moves. Assessment before rebuild.

Advisory and strategy

Cowork Activation

Turns Anthropic's Cowork into measurable outcomes. Leaders first, then the whole organization, then function-specific skills and connectors the team runs every day.

An open-source (MIT) framework for Claude Code: what is being built is agreed before any code is written, and the human decides at every step. Supports task decomposition, scoped execution, and verification across the AI engineering lifecycle.

Our assessment: Flagship open-source framework with clear method (human-in-the-loop at every step), MIT-licensed, and purpose-built for Claude Code — credibly productized.

The company AI Provectus runs on itself. Joins meetings and reads tickets, docs, deals, and Slack, then turns what is said into updated records and answers that carry sources.

Our assessment: Described as in production at Provectus, giving it credible proof of substantiveness; serves as both a product and an enterprise AI application reference.

Audits a repository or whole portfolio across twelve dimensions of agent readiness, returning gaps, a score, and a roadmap to close them.

Our assessment: Well-defined readiness audit with a named twelve-dimension methodology and scored output — substantive and differentiated.

Secrets, dependencies, and agent tooling gated at every place code moves — from local IDE to CI/CD pipeline — using Semgrep through MCP.

Our assessment: Branded capability with a clear technical method (Semgrep via MCP), but depth of productization is moderate based on available description.

Turns a legacy codebase into an agreed specification, a controlled rebuild, and proof that the replacement works. Combines AI reading the legacy system with human-approved rebuild stages.

Our assessment: Named framework/toolkit with a clear three-stage process (spec, rebuild, proof), but limited detail on tooling depth.

A role's data and methods connected to agent work: one screen where specialists decide on checked evidence, with sources attached. Available as an interactive demo.

Our assessment: Interactive demo available suggesting a built artifact, but described primarily as a UX pattern rather than a fully productized platform.

Incoming email read, matched to the case it belongs to, and routed to the owner with the context to act.

Our assessment: Named capability but very lightly described — one sentence with no method or proof signals.