We build AI agents that plan, complete and check work across your ERP, CRM and data systems. Every action has clear approvals and audit records, and it all runs in your private cloud.
What's included
6 capabilities, delivered by one squad. Use what you need now and add more as you grow.
The agent picks the right tool and calls your SaaS and internal systems only through approved actions.
Workflows that save their progress, retry failed steps and pick up where they left off.
Results are checked before any write, using schema checks, RAG checks and your business rules.
Roles, approvals and audit logs that let agents work in regulated teams.
Agents look up your policies and standard operating procedures (SOPs) before acting, instead of guessing.
Secure connectors link agents to the systems you already use, and every outbound call passes through a proxy you control.
Our approach
The usual way
Agents call APIs with no permissions, role checks or limits on what they can change.
How we do it
Every action passes our controls first: role-based access, a list of allowed actions, rate limits and approval steps.
The usual way
Agents lose track of long tasks and stop halfway through.
How we do it
Long tasks save progress at checkpoints and retry failed steps, and every action is safe to run again.
The usual way
Outputs go unchecked, so errors flow straight into your operations.
How we do it
Before an agent writes anything, its output is checked against the expected format, your business rules and the source documents.
Architecture
Each layer has a clear job, so the system is easier to secure, test and extend.
Secure access to your SOPs, policies, tickets, CRM records and operational data.
Each task runs as a set of defined steps, with saved progress and automatic retries.
Plans the task, chooses tools and checks each result against your rules and approval gates.
Every agent run is traced and logged with its cost, so you can see what happened and improve it.
How we deliver
4 phases, each ending with an output you can review.
We find the workflows worth automating, map tool access, set action policies and agree what each task must be checked against.
Output: Feasibility blueprint
We set up role-based access, allowed actions and approval steps, then connect agents to your systems with credentials limited to each task.
Output: Clear limits on what agents can do
We build the agents and their checks, so each result is validated before anything is saved to your systems.
Output: Agent ready to go live
We roll out with tracing, logs, cost controls and run reviews, then extend agents to more workflows and teams.
Output: Automation you can measure
Your team
Specialists join your squad for this work, alongside a delivery lead who keeps you updated.
Designs how each agent plans its work, which tools it can call and what it does when a step fails.
Builds the checks every result must pass: data formats, business rules and evidence from your documents.
Watches agent runs, keeps costs within budget and reviews failures to improve the next version.
Models threats, defends against prompt injection and tool misuse, and writes the rules agents run under.
Trust and control
Format, policy and evidence checks must all pass before an agent saves a change.
Allow-lists, RBAC, approval gates and scoped credentials control how agents use each tool.
The models run inside your cloud, and tamper-resistant logs record every decision and action.
A person reviews and approves higher-risk actions before they run.
You keep full ownership of the code, configuration and documentation we create, with no vendor lock-in.
We pick what fits your product and team, not the other way round.
Results
We built an internal AI operations agent for a growing B2B company. It processes incoming requests, chases missing information, updates business systems, routes work, follows up automatically and flags exceptions for a person to review.
We built an AI agent that reads incoming documents, captures the key details and checks them against business rules. It updates internal systems, asks for missing information and sends exceptions to the right person for review.
We built an AI sales agent for a B2B company that replies to inbound leads, works out what they need and qualifies the opportunity. It updates the CRM, prepares follow-ups and passes high-value prospects to the sales team.
For a large health system, we built a claims platform that matches EHR evidence to payer rules, automates routine authorisations and logs every decision. It is built to HIPAA requirements. Claims recovery rose 42%, and staff saved 15k hours.
AI agents suit multi-step work that spans several systems: handling incoming requests, reading documents and checking them against business rules, qualifying sales leads and updating your ERP or CRM. Our case studies include a back-office operations agent, a document approval agent and a sales qualification agent, each sending exceptions to a person.
An agent can only take the actions you have approved. Each one gets a list of allowed actions, role-based access and credentials scoped to its task, plus rate and budget limits. Higher-risk actions wait for a person to approve them, and every attempt is logged.
Every result is checked before anything is written. The agent's output must match the expected format, pass your business rules and be backed by evidence from your documents. If any check fails, the action is stopped and the case goes to a person for review.
Agents can run fully inside your environment. Model inference and tool calls stay in your private cloud, and logs and context data sit under your own security controls. Outbound calls pass through an egress proxy over mTLS, so you decide which outside services an agent can reach.
Long tasks run as a series of saved steps. The workflow records its progress at checkpoints, retries a failed step after a short wait and resumes from the last good point. Each action is designed to be safe to repeat, so a retry doesn't send the same update twice.
Each agent has a cost budget, and every run is traced with its tool calls, results and spend. Dashboards and alerts show when an agent costs more than expected, and regular run reviews find failures and waste, which we fix in the next release.
Timing depends on how many systems the agent must use, how many checks each task needs and how much human approval is involved. We start with a workflow audit and tool mapping, which gives you a feasibility blueprint and a plan. A scoped project can kick off in 1–2 weeks.
More in AI & Automation
An AI strategy and policies that turn into working approvals, access rules and audit records.
Learn more about AI strategy and governanceVision systems that detect, track, read and inspect on your own devices, with checks and monitoring.
Learn more about Computer vision and edge AIChat and voice assistants that answer from your knowledge, take approved actions and are tested before release.
Learn more about Conversational AI and NLPPrivate LLMs and RAG systems built on your data, hosted in your cloud and owned by you.
Learn more about Generative AI and LLM developmentWorkflow, document and integration automation with exception handling, approvals and monitoring.
Learn more about Intelligent process automationForecasting, anomaly detection and scenario planning built on reliable data and monitored once live.
Learn more about Predictive analytics and data scienceTell us what you need. We'll suggest the right team and a rough quote range, and an NDA is available before you share anything sensitive.