Workflow Products
Pick one evidence heavy workflow, connect the sources and turn preparation into reviewer ready action.
Bid response, estate review, Copilot readiness, RCA, access and customer operation queues.
See the products →AI workflow engineering
Take today’s workflow. Compress it by 10x. That's a Tesrex AI workflow.
Tesrex builds the operating layer around the model: source ingestion, RAG, LLM platform, reviewer queues, evidence packs and team handover.
Source to actionReviewer ownedReusable engine
What gets compressed
One current product shape is RFI/RFP preparation: import the package, extract the required responses, retrieve approved material, expose gaps and give the reviewer a pack that no longer starts from a blank page.
- Import
- Package, questions and response locations.
- Retrieve
- Approved answers and supporting material.
- Mark gaps
- Unknowns, assumptions and owner actions.
- Review
- Named reviewer pack before submission.
TESREX
10x Faster
Evidence backed
Start where evidence already slows the team.
Choose a workflow with messy sources, a clear decision owner and a human review step. That is where AI can prepare the work without pretending to own judgement.
Model choice comes after source quality, retrieval, context design, review UI and adoption.
Build AI Platforms
Build the product layer around the LLM: ingestion, embeddings, RAG, model controls, sidecar checks and role based UI.
Source connectors, reviewer queues, workflow state, evidence packs and feedback.
See the platform work →Modernise Teams
Train leadership, engineers and operators on the habits that make AI dependable in live work.
Context windows, retrieval, prompt systems, reviewer habits and escalation boundaries.
Plan team adoption →Estate to Evidence
Modernise Cisco, Microsoft, security and collaboration estates so AI systems can trust the underlying data.
Copilot source quality, Secure Access, Cisco evidence, permissions and telemetry.
Review the estate →Private AI is a build route, not a badge.
Tesrex designs the boundary first: which sources can enter RAG, where customer data is allowed to live, how context windows are assembled, which temperature and model routing controls are exposed, and where sidecar or dual model review gates outputs before action.
Source governanceModel controlReviewer gates
Open Private AI Engineering →Finance
Private retrieval over policies, approvals, regulated document review, investment/commercial packs and role based reviewer queues.
Healthcare
Approved source workflows for knowledge bases, service summaries, escalation notes and governance review inside clear data boundaries.
Technology
Open weights or private model routes, multimodal ingestion, sidecar evaluation, context window design and controlled deployment paths.
The workflow still belongs to the team.
AI prepares the draft, the evidence pack and the next action. People keep the source judgement, reviewer notes and handover path visible.
Source materialRecords, telemetry, platform data and documents that can be checked.
Review pointAssumptions, gaps and changes stay visible before action is routed.
Reusable layerThe first product leaves ingestion, RAG context, reviewer queues and feedback behind.
From source material to reviewed action.
The useful product is the decision trail: approved sources, retrieved context, model prepared work, reviewer notes, routed action and feedback.
That trail makes an AI workflow easier to govern, improve and hand over.
SourcesApproved documents, portals and platform data.
ContextRetrieval, model settings and gap labels.
ReviewEvidence, assumptions and risk.
ActionWorkpack, queue and owner.
FeedbackReviewer changes tune the next pass.
A workpack leadership can act on.
Tesrex turns workflow friction into a visible workpack: the sources, risks, route and adoption path behind the next AI move.
The output helps leadership decide whether to train, prototype, build, modernise or stop.
How the workpack is built
IntakeThe live package, records, instructions and owner context.
StructureQuestions, response locations, decisions and exclusions separated early.
RetrieveApproved material assembled with clear boundaries and traceable references.
DraftPrepared answers, notes and partial responses for review, not blind completion.
Gap routeUnknowns, assumptions and owner actions raised before submission or handover.
Review packNamed human checks, changes and final judgement kept visible.
Leadership Workpack
A concrete deliverable from the review, not another slide about AI potential.
- Priority workflow mapWhere AI can compress work first, ranked by impact and feasibility.
- Material and boundary inventoryApproved material, data boundaries, permission gaps and governance constraints.
- Reviewer-owned prototypeA practical output matched to the workload, with gaps kept visible.
- Training and adoption pathReviewer habits, escalation boundaries and team readiness for live AI assisted work.
Bring one workflow with real friction.
The review starts with your actual sources, your actual decision owner, and the one workflow where evidence is already slowing the team. We will map the sources, permissions, context window, model/review boundary, first workpack and handover plan.