AI and generative AI solutions

The gap between a convincing AI demo and a system a business can rely on is entirely in the parts a demo skips: what happens when the model is wrong, where the knowledge comes from, who is allowed to see it, and how you know next month whether it is still working. We build the whole thing. Customer-facing chat and voice agents, internal assistants grounded in your own documents, and generative features inside existing products — each with an evaluation set, a confidence threshold and a human path for everything below it. Northwind Diagnostics’ AI receptionist now handles 71% of inbound calls end to end; the other 29% reach a person faster than they used to, because the queue is shorter.

• 01

  A measured baseline before anything ships. We record what the current process costs in handling time, error rate and abandonment, so the improvement is a number rather than an impression.

• 02

  Grounded in your data, not the model’s memory. Retrieval runs against your documents and systems with per-user permissions applied at query time, so an assistant cannot surface a record its user could not otherwise open.

• 03

  An explicit escalation path. Every agent has a confidence floor and a hand-off to a human, and the hand-off carries the transcript so the customer does not repeat themselves.

• 04

  An evaluation suite that runs in CI. Prompt and model changes are tested against a fixed set of real cases, which is what stops an improvement in one area quietly breaking another.

• 05

  Cost and latency budgeted per interaction, with model choice reviewed against them. A cheaper model that is good enough for classification does not need to be the one writing the reply.

1. 01

   Use-case triage

   One week. We rank candidate use cases by volume, tolerance for error and how cleanly success can be measured, then take the one that scores well on all three. High-volume, low-stakes, easily measured is where a first deployment should live.

2. 02

   Baseline and evaluation set

   We measure the existing process and build a test set of real cases with agreed correct outcomes, including the awkward ones. Nothing goes to users before this exists, because otherwise there is no way to tell whether it worked.

3. 03

   Grounding and pilot

   Retrieval over your content with permissions enforced, tool access to the systems the agent needs, and a limited pilot behind a flag with a human reviewing outcomes daily.

4. 04

   Harden and integrate

   Prompt-injection and data-leakage testing, rate and cost controls, fallbacks for provider outages, and integration with the CRM, telephony or helpdesk the result has to land in.

5. 05

   Operate and re-measure

   Dashboards for deflection, escalation and cost per interaction, and a monthly review against the baseline. Model providers ship changes; a system nobody re-measures degrades silently.

• The deployed assistant, agent or generative feature, with source in your repository.
• Evaluation suite with scored real cases, runnable in CI on every prompt or model change.
• Retrieval pipeline and index over your content, with the permission model documented.
• Baseline-versus-current report: handling time, deflection rate, escalation rate, cost per interaction.
• Safety review covering prompt injection, data leakage, and the failure path when the provider is down.
• An operating guide naming who reviews what, how often, and what triggers a rollback.

• 01Will our data be used to train someone else’s model?

  Not under the arrangements we set up. We use enterprise API tiers whose terms exclude training on customer data, and we document which provider processes what and where. Where the data cannot leave your tenancy at all, we deploy models inside your own cloud subscription and design for that constraint from the start rather than discovering it at review.

• 02What happens when the model gets something wrong?

  It is designed for, not hoped against. Every agent has a confidence threshold below which it hands off, high-consequence actions require a human confirmation, and everything is logged with the retrieved context so a wrong answer can be traced to its cause rather than argued about. The escalation rate is a headline metric, not a footnote.

• 03Can you build a voice agent that answers our phones?

  Yes — that is one of the most common engagements here. It answers, identifies the caller, handles the routine categories end to end against your systems, and transfers with the transcript attached when it cannot. It runs alongside your existing line during a pilot so you can compare handling and abandonment before committing.

• 04How much of this is a licence cost we carry forever?

  We budget cost per interaction in the pilot and show it to you monthly. For most workloads the model spend is smaller than expected and the integration is the real cost. Where volume makes inference the dominant line, smaller models or a self-hosted deployment become worth the operational overhead, and we will tell you when you cross that point.

• AUT

  Business automation

  Removing the manual steps between systems — workflow, document handling, sales and service processes, with or without AI.

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• API

  API and system integration

  Making the systems you already own talk to each other — payments, CRM, ERP, messaging and the third-party APIs in between.

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• CSD

  Custom software development

  Business systems built for how your operation actually runs, when the off-the-shelf product would need bending until it breaks.

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