APPLIED AI LAB

Turn AI opportunity into working proof

Build what needs to be proven. Learn what analysis cannot tell you.

Hands-on AI engineering, architecture, data, and integration expertise for the opportunities where working software is the most credible way to reduce uncertainty.

See How the Lab Works
Illustration of four AI engineers collaborating at a shared workstation, coding, examining a luminous neural model, and reviewing technical results.
One team. The full technical stack.
  • Models + agents
  • Architecture
  • Data + integration
  • Evaluation

WORKING AI AS TECHNICAL PROOF

Resolve the questions that cannot be answered on paper

Will the model perform well enough? Can the data support it? Can an agent act safely in the real workflow? Working software tests these assumptions before the organization commits scarce production resources.

  1. 01

    Access

    Specialized capability, when you need it

    Bring AI engineering and architecture into the investigation while internal teams stay focused on production.

  2. 02

    Accelerate

    Move from concept to working learning

    Use the fastest credible technical path to see what actually happens.

  3. 03

    Prove

    Test the assumptions that matter most

    Focus the build on model behavior, grounding, integration, reliability, and workflow fit.

  4. 04

    Learn

    Expose the realities behind the concept

    Discover data issues, user friction, operating constraints, and hidden dependencies.

  5. 05

    Transition

    Leave with a clearer production path

    Carry working software, technical findings, and updated requirements into the next decision.

HANDS-ON AI ENGINEERING

Deep enough to test the real solution

Business-value discipline backed by real AI engineering. Trebor designs, builds, integrates, and instruments the technical stack needed to turn a concept into a credible working solution.

Experience + workflow

AI applications, copilots, assisted decisions, human-in-the-loop experiences

Agents + models

Tool use, orchestration, prompt and context engineering, model evaluation

Knowledge + retrieval

Enterprise grounding, search, RAG, context assembly, source quality

Data + integration

APIs, connectors, Dataverse, enterprise systems, data transformation

Architecture + operability

Identity, permissions, security, human oversight, production readiness

Evaluation + learning

Quality, latency, failure modes, observability, workflow and adoption signals

TOOLS + MODELS WE WORK WITH

Use the technology that best answers the question

Enterprise platforms and AI models selected for your use case, environment, and learning objective.

Build the system. Inspect the behavior.Models, knowledge, and integrations tested together.
Illustration of two AI engineers inspecting a layered, working AI system above a technical testbench, with neural processing, connected knowledge stores, code, and evaluation displays.

FROM BUSINESS NEED TO WORKING AI

Built around your business
connected to your enterprise

The Lab brings your systems, data, knowledge, and AI capabilities together in a working solution shaped around the AIF use case.

An AIF-defined business use case guides the solution. Enterprise systems, operational data, and knowledge feed AI capabilities and an agentic workflow. The solution supports people, automation, decisions, and action. Measurement and learning feed back into the enterprise context.

AIF-defined business use case

Problem · Workflow · Owner · Desired outcome

Enterprise context

  • Business systemsCRM · ERP · ITSM · Finance
  • Operational dataDatabases · APIs · Events
  • Enterprise knowledgeDocuments · Policies · History

Purpose-built AI solution

Technology selected to fit the need.

  • Knowledge + retrievalGround AI in trusted enterprise context.
  • AI modelsLanguage · Vision · Prediction

Agentic workflow + orchestration

Business workflow

  • Assist peopleInsights · Summaries · Copilots
  • Automate workTasks · Routing · Execution
  • Improve decisionsPredict · Rank · Recommend
  • Take actionUpdate · Trigger · Communicate

TECHNICAL LEARNING + EVIDENCE

Learning that guides the next step

Testing this solution in your environment turns technical learning into evidence to refine the AIF use case, plan for scale, and accelerate its expected outcomes.

  • Technical + workflow findings

    What worked, what failed, where users gained value, and what remains uncertain.

  • Architecture + production requirements

    Models, data, integrations, controls, monitoring, support, and ownership.

  • Updated delivery assumptions

    A more credible scope, cost, timing, dependencies, and team requirements.

  • An evidence-backed recommendation

    Progress, refine, re-plan, or stop—with a clear basis for the next commitment.

BEYOND TECHNICAL PROOF

Building unlocks more than a working solution

A focused PoC or pilot gives your organization something real to work with—as the AIF use case moves toward scale.

  • Build confidence and alignment

    Give leaders and teams a shared understanding through direct experience.

  • See potential value sooner

    Make future ROI assumptions tangible and explore how changes in work could become business value.

  • Discover better possibilities

    Hands-on use reveals valuable capabilities and better approaches beyond the original concept.

  • Develop people and adoption

    Shape the solution with users while building practical skills, trust, and ownership.

  • Make progress with focused capacity

    Add specialist engineering and rapid iteration while protecting internal production priorities.

  • Strengthen what comes next

    Carry useful components, knowledge, and working practices into delivery and future AIF opportunities.

A STRONGER FOUNDATION

Each implementation strengthens the opportunity—and your capability to realize value from AI.

Concept illustration of a business leader and an engineer discussing an illuminated cyan and orange glass model in an AI lab.

START BUILDING WITH THE APPLIED AI LAB

Advance your AI opportunity with working AI

Bring us the use case you want to advance. We’ll scope a focused build within AIF, combining specialist engineering with your data and workflow to test its potential and clarify the path to scale.

Talk to Trebor

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