LLM Integration that lives inside the tools you already use.
Build · 4–8 weeks · fixed priceNot another standalone chatbot. We embed language models into your CRM, inbox, docs and support surfaces — with evaluation, guardrails, cost budgets and clear rules for when to refuse.
A standalone “AI chat” is the wrong shape for most companies.
The data the model needs to be useful is already in your CRM, inbox and docs — not in a separate window.
Off-the-shelf chat answers questions it shouldn't, in voices that aren't yours, with no audit trail.
Without per-feature cost budgets and observability, LLM spend grows quietly until it hurts.
Models in the workflow — not bolted on top.
- Surfacing
- LLM features inside your existing tools — inbox, ticket view, doc editor, CRM record
- Guardrails
- Voice/style guides, allowed-topic rules, refusal contracts, PII handling
- Evaluation
- Golden cases per feature, regression tracking, on-call alerts when quality drifts
- Cost control
- Per-feature spend caps, model-tier routing, observable token usage
- Portability
- Provider-agnostic adapters — you can change LLM vendor without rewriting features
LLM features your team uses without thinking about them.
The win is invisible: the model is in the tool, the suggestion appears in the right place, the team accepts or rejects it, and you have data on whether it's helping. That's the bar.
Stronger together with retrieval and automation.
RAG Knowledge System
Most LLM features benefit from retrieval over your sources rather than open-ended generation.
/services/workflow-automationWorkflow Automation
Models are at their best where a reliable workflow already routes work to them.
/services/custom-ai-agentCustom AI Agent
When a feature owns a multi-step job end-to-end, it tends to become an agent.
Embed models where work happens.
We start with an audit to pick the right surfaces. Then we ship — with evals, guardrails and budgets in place from day one.