AI assistants and agents that work in production: they read your data, act on events and run multi-step work, with a person approving what matters.
Most organisations have tried AI tools. Far fewer have made them work at scale, because what fails is rarely the model: it is the context, the connections and the trust.
Staff do not trust answers they cannot check, so they go back to what they know and adoption stalls.
Poor context design causes wrong answers, irrelevant retrieval and broken hand-offs between agents.
Single AI tools do not add up to real gains without rules, memory and checks joining them together.
Most work that AI can take on falls into four shapes. We build each of them on the same foundations, so they share your knowledge, your systems and your rules.
Answers from your policies, documents and systems, with the source beside every answer, in the tools your people already use.
For exampleLeave balances, policy questions and HR letters, inside Outlook or Teams.
Agents that watch an inbox, a system or a sensor and act when something happens, without anyone asking.
For exampleA late delivery triggers an email to the supplier and a note on the order.
Work that crosses several systems, done step by step, and paused for a person wherever a decision matters.
For exampleAn invoice arrives, is matched to its order, and is posted after approval.
Agents that talk, on the phone or on a device, with replies fast enough to feel like a conversation.
For exampleBookings and common questions by phone, in English and Spanish.
The best first agents take on work that is frequent, rule-based and spread across systems. These are the ones we are asked for most, team by team.
Salary letters, employment certificates and leave answers, generated from the ERP in seconds.
New starters get their accounts, documents and first-week plan without a chase.
Invoices read, matched to orders and deliveries, and queued for approval with any mismatch flagged.
Figures pulled from each system into the report your board already reads.
Common questions answered at once; the rest sorted, summarised and sent to the right person.
Enquiries qualified around the clock, logged in the CRM, with follow-ups drafted for the team.
Reports turned into work orders, patterns spotted across sites, and the next service flagged early.
Clauses compared with your standard terms, with every difference listed for a lawyer to decide.
Plain questions about performance answered from your own databases, with the chart and the source.
Four phases with one thread: context designed from the start, so what works in week two still works a year later.
We find the highest-value work per team and measure how it is done today, so gains can be proven.
You getA baseline to prove the gains
Retrieval, knowledge and memory design, plus the harness around each model: rules, tools, checks and MCP connectors to your systems.
You getAn architecture that keeps answers reliable
Assistants that answer, proactive agents that act on events, and multi-step workflows that pause for human approval.
You getAgents running on real work
Training, AI champions and governance, then dashboards on usage and quality. We stay until the numbers move.
You getAdoption you can measure
Every engagement leaves agents in production, the connectors they use and the dashboards that show they are working.
We choose the models and where they run for your data and your rules, then connect them to the systems you already have.
Our own products and delivery run on agents, so our advice comes from operating them, not from reading about them.
Retrieval, memory and the harness around each model are designed up front, because that is where most pilots break.
Agents pause for a person before they send, pay or change anything that matters, and every action is logged.
An assistant we built and run is used every day by more than 2,000 staff.
staff using one AI assistant we built and run
for a salary letter from the ERP, signed and emailed
an agent answers buyers and collects their details as leads
Vendor-neutral advice on which AI fits which problem, from use case mapping to a solution architecture a team can build from.
Explore→Private AIThe assistants and agents your team wants, running where your data already lives: your servers, your private cloud, under your control.
Explore→Technology ConsultingA multi-year AI roadmap your leadership agrees on, with governance that lets teams move rather than holding them back.
Explore→Only where you allow it. Anything that matters waits for a person to approve it.
Yes. We connect them through secure tool connectors, including MCP, with access limited to what each agent needs.
The rules, tools, memory and checks around a model. It is what makes an agent reliable enough to run every day.
The ones that fit the task and your rules: frontier models where quality matters most, open-source models where cost or privacy does, and models on your own servers when data cannot leave.
Only where you agree. Agents can run in your own cloud or on your own servers, personal data is masked before it reaches a model, and nothing is used to train anyone else’s model.
Anything that matters waits for a person, every action is logged, and answers show their source. We measure quality from the first week and tune the agent on real cases.
Usually within weeks for a first, well-scoped use case, measured against how the work is done today.
It depends on the scope, and we agree it before any work starts. The first conversation is free.
Tell us where you want to be. We will show you the fastest way to get there, with the right mix of strategy, technology and AI.