Bring agentic AI into your business with the controls to actually trust it.
Generative AI is impressive, but agentic AI—systems that can reason, use tools, and take action—is transformative. We build governed multi-agent systems that connect LLMs to your actual operational data without hallucinating or breaking things.
What we deliver
- Custom internal AI assistants grounded in your proprietary data
- Multi-agent systems built on LangGraph for complex, multi-step workflows
- Strict tool access boundaries using the Model Context Protocol (MCP)
- Comprehensive AI Ops governance, audit logging, and safety frameworks
Common engagement patterns
Common scenario: Internal Q&A agent over your business's own knowledge.
Typical approach: Bridges LLMs with your real data stores (Confluence, SharePoint, Jira), enforcing existing access controls and generating audit trails for every query.
Typical timeline: 3-5 weeks
Indicative outcome: Organizations deploying internal knowledge agents see a 20-30% reduction in time spent searching for information (McKinsey).
Common scenario: Multi-agent workflow automating a full operational process.
Typical approach: Designed using LangGraph and MCP, with each sub-agent's role, tool access, and decision logic explicitly defined and reviewable.
Typical timeline: 6-12 weeks
Indicative outcome: Agentic automation of multi-step operations can reduce processing times by up to 60% while maintaining accuracy (Forrester).
Common scenario: Augmented decision-making for your team.
Typical approach: AI assistants that analyze complex datasets and recommend actions, but require human approval on every consequential loop.
Typical timeline: 4-8 weeks
Indicative outcome: Augmented decision support increases operator throughput by 35% without bypassing human accountability (Gartner).
How we approach it
Use case identification
We evaluate your operations to find high-impact, low-risk areas where an agentic system can drive immediate value.
Sandbox prove-out
We build a prototype isolated from your production data to prove the agent's logic, reasoning, and tool use.
Production with controls
We deploy the agent with strict Model Context Protocol (MCP) boundaries, audit trails, and human-in-the-loop safeguards.
Operate
We monitor the system's performance, handle model upgrades, and refine the agent's instructions based on real-world edge cases.
Tools we use
We are model-agnostic but strongly prefer Anthropic's Claude family for complex agentic reasoning tasks.
Ready to start?
Assess your readiness for agentic systems, or speak directly with Bennet.