Problems we solve / AI

02 · Applied AI

AI, applied where it matters.

We start with the business problem, shape the right AI solution around the workflow, and measure the business outcome — while proving every decision the system touches.

GenAIAI AgentsDocument intelligencePrediction modelsAutomation

Why it's hard

Judgement, twice over.

First decide where AI genuinely belongs. Then make sure every answer and decision can stand up to scrutiny.

Where it belongs

Value often hides in documents, narratives and free text.

Not everywhere

Technical possibility still needs business judgement.

Provably right

Once AI touches a decision, “usually right” is not enough.

Auditable

You need to prove how the answer was reached.

How we approach it

Start from the problem.
Work back to the AI.

The model is only one part of the path. The real work is deciding where AI adds value, grounding it properly and embedding it into a decision you can trust.

Applied AI journey

From business problem to auditable decision

AI is shaped around the workflow — not dropped on top of it.

Business problem Start with the outcome

AI-shaped part Find where it changes the work

Grounded in data Context, retrieval and tools

AI embedded Inside the real workflow

Human validation A person stays in the loop

Auditable decision Explainable and defensible

Applied across Applications Operations

Reads what rules can't. Makes decisions accountably. Lets teams ask questions over real data — with answers that can be traced and checked.

Selected work

See the before. See the after.

Swipe or scroll through the transformations to see what changed — from the original problem to AI embedded in the workflow.

Machine learningConsumer services

Real-time lead scoring, before it reaches a rep

One heuristic qualified every lead

Good leads could be buried

Sales capacity was wasted

Real-time ML scoring

Retraining built into the loop

Human validation gate

Extended across all divisionsRollout expanded beyond the initial implementation

Applied AIIndustrial parts distribution

The AI recommendation engine behind $6.5M in incremental margin

Buying lists looked only at historic sales

Valuable supply channels went underused over trust concerns

Three algorithms surface and rank deals daily

AI screens seller trust and likely defects before buyers see them

$6.5Mincremental gross margin

16Xreturn on program fees

Under the hood

The capability map behind applied AI that holds up.

A compact view of the model, grounding, reliability and machine-learning capabilities behind production AI.

Models + serving

LLMsAmazon BedrockModel routing

Grounding + retrieval

RAGTool useVector searchMCP

Ground Evaluate Observe Validate

Production
Applied AI

Unstructured data

Document understandingExtractionClassification

Reliability + ML

Evaluation harnessesObservabilityHuman-in-the-loopPredictive modellingMLOpsFeature pipelines

The technology changes by use case. Grounding, evaluation and accountability do not.

See all AI case studies →

Precondition

Applied AI is only as good as the data under it.

Grounding needs something solid to ground to. The best AI work sits on a foundation you can already trust.

Data foundations you can trust →

How we build

Every engineer builds with AI in the loop

And a human signs off every gate. That's what makes it faster without being reckless.

How we deliver →

Next step

Where would AI actually move the needle for you?

We start by understanding the problem — then find the part where AI genuinely changes what's possible. Not the other way round.

Start a conversation →