AI Agents and Assistants
AI that works from your own data, connects to your tools and stays under your team's control.
A business AI agent is much more than a chat window. It finds checked information in your own knowledge base, does set tasks in your CRM or helpdesk and passes anything unusual to your team. We deliver reliable agents for sales research, customer support and internal operations, with strict access rules and a full record of what they do.
What we start from
- Defined workflow
- Approved data
- Access limits
- 1Scope
- 2Integrate
- 3Govern
- Working agent
- Human approval
- Audit trail
What you own
Why off-the-shelf AI tools fall short.
Knowledge is scattered.
Policies, product details and past answers sit in wikis, tickets and people's heads, which slows down new hires and replies.
Prep work eats your best people's time.
Sales and support teams spend hours gathering context before they can act on a customer request.
Generic chatbots hurt trust.
Bots that aren't connected to your systems guess at answers and can't safely complete real tasks.
What we deliver.
Knowledge assistants with sources
Internal assistants connected to your docs, procedures and past tickets that answer fast and link to the source.
Account and deal briefs
Agents that collect company data, recent news and CRM history into a brief before each call.
Support sorting with human approval
Tickets are sorted and routed automatically, with draft replies your team approves in one click.
Safe actions in your tools
Agents can read and update CRM records, helpdesks and databases, only within the permissions you set.
Rules and audit logs
Checks on what goes in and out, confidence limits, removal of personal data and a full log of every conversation.
Quality and cost tracking
Dashboards for answer accuracy, speed and AI cost per workflow.
Tools we work with
- OpenAI Enterprise
- Anthropic
- n8n
- LangChain
- Pinecone
- HubSpot
- Slack
- Zendesk
How we take an AI agent from idea to live use.
- 01
Pick the use case
We choose one valuable workflow, set accuracy targets and agree on security limits.
- 02
Implement and validate
We implement knowledge retrieval, system integrations and safeguards, and validate them against real historical cases.
- 03
Pilot
A careful rollout to a small group of users to fix edge cases and the approval flow.
- 04
Run and tune
Ongoing accuracy checks, knowledge updates and cost control.
Focused scope. Clear return.
- Project
AI agent
One AI agent connected to your data and systems, with safety checks, a test set and a pilot rollout.
Scope an agent - Retainer
Managed AI agent
Ongoing accuracy checks, test updates, knowledge syncs and new features.
Discuss a retainer
AI agent questions.
How do you choose the AI model?
We don't tie you to one provider. We usually choose between OpenAI, Anthropic and private models based on accuracy, speed, compliance needs and cost. The setup lets you switch models later without a rebuild.
How do you stop the agent from making up answers?
Agents may only answer from documents they are allowed to use, must show their sources and hand over to a person when they are unsure. We test every release against real past cases.
Can everything run inside our own cloud?
Yes. If you have strict data rules, we can run the whole system, including search and models, inside your own AWS, Azure or Google Cloud account.
What does it cost to run after launch?
AI and API costs grow with usage and are usually much lower than the staff time saved. We estimate monthly running costs up front, and add spending limits and dashboards when it goes live.
Related services.
Use AI with the controls your business needs.
Book a consultation to identify the AI use case with the greatest impact on your business.