AI Solutions & Automation
We put AI and automation where they shorten real workflows: triaging tickets, drafting replies humans edit, searching internal docs, or routing work to the right queue. We stay skeptical of hype, tight on measurement, and explicit about when a human must stay in the loop for compliance or quality.
- One pilot tied to a metric you already track: time, accuracy, volume, or cost
- Grounding and access rules so sensitive data does not leak through prompts
- Fallback paths when the model is wrong, offline, or an API times out
Where AI actually pays off
The wins are usually boring: fewer clicks between tools, faster first drafts for humans to edit, better routing so Tier 1 stops bouncing complex cases. We interview people who do the job today and prototype against real ticket or doc samples, not toy paragraphs.
Automation without AI still matters. Scheduled jobs, webhooks, reconciliation checks, and alerts that fire before a customer notices often beat a flashy chat widget nobody trusts.
How we keep scope honest
We agree on one workflow or question type for the first release. You see it running against production-shaped data in a controlled environment before we widen permissions or spend.
Models drift and vendors change limits. We plan evaluations: spot-check outputs, log retrieval sources where relevant, and revision prompts or filters when quality slips. That is ongoing hygiene, not a one-time launch party.
Security and governance
We document who can access which data, retention expectations, and regions involved if you operate under strict rules. If you cannot train on customer content, we engineer around that instead of pretending the problem goes away.
When third-party APIs fail, users still need an answer. We design degraded modes: cached responses, queues for retry, or a clean handoff to a human with context attached.
Stack and integration
We integrate with the systems you already pay for: CRMs, help desks, ERP hooks, and internal wikis. The glue code is as important as the prompt text if you want adoption outside the engineering team.
Related services
Common questions
Will you train models on our private data?
Only if you explicitly want that and your agreements allow it. Many engagements use retrieval over your documents without retaining customer payloads for training. We spell out data flow in writing before build.
What does a pilot cost and how long does it run?
Scope depends on systems touched and risk level. We quote a bounded pilot with a fixed timeline and success criteria, then decide whether to expand. If we cannot define “better” in numbers, we pause instead of burning budget.
Can you help if we already bought an AI tool but nobody uses it?
Often yes. We map why adoption failed: trust, latency, wrong workflow fit, or missing integrations. Sometimes the fix is product glue and training, not a fancier model.
Do you only work with OpenAI or Claude?
No. We pick providers and models based on quality, cost, latency, and your compliance posture. Open-source and hosted options are on the table when they fit.
Want to talk it through?
Send your timeline, stack, and what success looks like on your side. We reply with specific questions and a next step, not a generic deck.
Contact Boltout