Standalone AI tools just create another tab nobody opens. We integrate AI directly into your CRM, ERP, support desk and internal apps, grounded in your own data and governed by your own rules.
Overview
A model behind a separate login changes very little. Value appears when the summary is already in the ticket, the draft is already in the CRM, and the answer cites your own documentation.
We build the retrieval, the connections and the guardrails that put AI inside existing screens, with logging, cost control and evaluation in place from the first release.
Add drafting, summarising, classification and extraction to the screens where the work already happens.
Turn your documents and records into a retrieval layer so answers cite approved sources instead of guessing.
Enrichment, scoring, next-best-action and automatic notes inside Salesforce, HubSpot, Dynamics or your own system.
A clean, versioned service layer so every system consumes AI through the same governed path.
Test sets, quality scoring, cost dashboards and alerts, so output quality is measured rather than assumed.
Data minimisation, redaction, role-based access, retention rules and data residency options.
Architecture
The model you choose today will not be the best one in a year. Anything hard-wired to one provider quietly becomes a migration project.
We put a routing and abstraction layer between your systems and the models, so you can switch providers, run private models or mix them by task without touching your applications.
Impact
Adoption is the real metric. AI that requires a detour gets used once; AI inside the workflow gets used all day.
Research, drafting and classification collapse from minutes to seconds.
AI appears inside the tools your teams already have open.
One controlled path to models, with cost and access under your rules.
Every output traceable to its prompt, its sources and its reviewer.
Document review, summarisation and risk flags with every source and decision recorded.
Notes, letters and coding drafted from existing records, always confirmed by a clinician.
Answers grounded in live product, stock and policy data instead of a generic model.
Triage, translation and drafting inside case systems, with a person approving every outcome.
No. We use enterprise endpoints and configurations where your inputs and outputs are excluded from training, with retention set to the minimum the workflow needs. Where policy or regulation requires it, we deploy open-source models in your own environment so nothing leaves your infrastructure.
Anything with an API, and most things without one. That includes Salesforce, HubSpot, Dynamics, Zendesk, Microsoft 365, Google Workspace, SAP, common ERPs, plus internal databases and custom applications. Closed legacy systems are handled through a bridge layer.
Retrieval-augmented generation constrains answers to your approved sources and returns citations, structured outputs are validated against schemas, and anything below a confidence threshold routes to a human. We also run test sets on every change so quality is measured, not hoped for.
Usually far less than expected, because most requests do not need the largest model. We route by task, cache repeated work, batch what can wait and set spending limits with alerts. You get a dashboard showing cost per workflow so the economics stay visible.
Yes, that is the point of the abstraction layer. Models sit behind a routing service, so swapping a provider, adding a private model or mixing them by task is a configuration change and a round of evaluation rather than a rebuild.
Two to six weeks for a single well-defined workflow, including retrieval, integration, evaluation and a supervised rollout. Broader programmes are phased so each integration proves itself before the next one is funded.
Show us your stack. We will map the integration points and tell you where AI actually pays.
