Make source material usable.
Bring portals, SharePoint, CRM, files, exports and telemetry into a governed intake path before the model is involved.
AI platform builds
Build LLM workflow platforms, not isolated prompts.
Tesrex builds the operating layer around LLMs: connectors, RAG and context assembly, model routes, reviewer queues, evidence packs, workflow state and feedback.
Source connectorsRAG contextPrivate/local routesReviewer queuesEvaluation harness
What Tesrex actually builds.
The credibility is in the working layer around the model: connectors, retrieval, controls, product UI, review state and quality feedback.
Specific enough for engineers. Clear enough for leadership to see the move from prototype to operated workflow.
Assemble evidence the model can use.
Design chunking, embeddings, vector search, source anchors and context window rules so output can be traced.
Choose the right route per workflow.
Use hosted, private, local or hybrid LLM paths, including open weights, sidecars, temperature controls and evaluator models where useful.
Give specialists a product surface.
Expose reviewer queues, assumptions, gaps, evidence packs, workflow state and approvals in a role based interface.
Keep quality visible after launch.
Capture reviewer changes, sidecar checks, failed outputs and reviewer corrections so the platform can improve safely.
Leave a system the team can run.
Package the prototype, operating model, training route and support boundary so the workflow survives handover.
The platform is the operating layer around the model.
A real build is not a prompt library. It is a product surface that connects sources, retrieval, model behaviour, human review, action routing and feedback.
The model prepares work. The platform makes the work reviewable, repeatable and safe to improve.
SourcesSharePoint, CRM, portals, files, telemetry.
Context and RAGChunking, embeddings, vector search, context windows.
Model controlsHosted, private, local/open weights, sidecars, temperature.
Reviewer surfaceQueues, evidence packs, action briefs and decision trail.
Deployment planHosted, private, local or hybrid by workflow.
Deployment is a design decision.
Model choice comes after the workflow, source boundary, evidence needs and reviewer path. Some workflows suit hosted services. Others need private deployment, local inference, open weights or a hybrid split.
Fastest when source risk and governance allow managed model services.
Useful when customer data boundaries, access controls or audit needs require a controlled tenant.
Relevant when data movement, latency or model control makes local inference worth the engineering cost.
Common in real platforms: different model routes for drafting, review, classification and evaluation.
Bring one workflow that deserves a real platform.
We will map the source connectors, context layer, model path, reviewer UI, evidence harness and adoption path before recommending the build.