Question 1

Which models do you use?

Accepted Answer

We are model-agnostic. Our default stack uses OpenAI, Anthropic and Google for frontier work; open-source models (Llama, Qwen, Mistral) when self-hosted inference is required for cost, latency or data-residency reasons.

Question 2

How do you handle hallucinations and accuracy?

Accepted Answer

With evaluation harnesses, retrieval grounding, structured outputs, human-in-the-loop checkpoints for high-stakes steps, and an explicit confidence-and-rollback design. We do not deploy generative AI into a workflow we have not instrumented for failure.

Question 3

Where does our data go?

Accepted Answer

Where you tell it to. We can architect for full data residency, run open-source models on your VPC, route through enterprise endpoints (OpenAI Enterprise, Bedrock, Vertex) with zero-retention policies, or any combination thereof.

Question 4

How do you measure ROI?

Accepted Answer

We baseline the workflow before automation (time, cost, error rate, throughput), then measure the delta after deployment for 60–90 days. Every AI engagement carries a measurement layer; we treat unmeasured AI as unfinished work.

Question 5

Can you build agents?

Accepted Answer

Yes — and we can also tell you when an agent is the wrong answer. Most 'agent' problems are better served by a deterministic workflow with one or two AI steps. Pure agentic architectures are appropriate for a narrower set of problems than the marketing suggests.

The questions buyers ask, answered straight.

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