You know it should change how work gets done and do not know which process to start with.
You need AI agents and integrations delivered to production, not another slide deck.
Staff paste customer data into a browser and paste the answer back — slow, unlogged, and risky.
You are comparing RPA bots with agentic AI and want someone who has built both.
Numbers from Gartner and the MIT NANDA report — the technology works; the process, the guardrails and the measurement around it are what usually go missing.
of generative AI pilots show no measurable return — the model worked, the process around it was never built (MIT NANDA, 2025).
Virtualization Review on the MIT NANDA report ↗or more of agentic AI projects will be cancelled by the end of 2027 over cost, unclear value or risk, Gartner predicts.
Gartner, June 2025 ↗vendors out of thousands offer real agentic AI, by Gartner’s count — the rest is “agent washing”.
Gartner, June 2025 ↗of companies have staff using personal AI tools, while only 40% bought an official subscription — no logs, no controls.
Virtualization Review on the MIT NANDA report ↗of small businesses name data privacy and security as the barrier to using AI.
Reimagine Main Street AI survey, 2025 ↗of organisations have made significant investments in agentic AI; 42% keep them conservative (Gartner survey, January 2025).
Gartner, June 2025 ↗Put your monthly volume into the calculator and see hours and dollars.
An AI automation agency builds language-model agents into the processes a business already runs — reading inbound emails and documents, pulling the structured data out of them, looking up context in the CRM and the knowledge base, taking the next action through an API and drafting the reply — with a person approving anything that carries risk. The difference from using ChatGPT in a browser is that nothing is copied by hand, every step is logged, and the agent works inside your systems rather than next to them.
We build AI automation around the tools you already use rather than a new platform. A request starts the run: a model classifies the intent and extracts the fields, code checks them, the agent reads the customer record from HubSpot, Salesforce or your ERP and searches your documents, then acts within the tools it is allowed to use. Low confidence, money and legal matters stop and go to a person in Slack or Teams. Corrections are collected into examples, so the agent improves instead of repeating the same mistakes.
Most AI pilots fail for lack of a process, guardrails and measurement, not for lack of a model. Agentic process automation done this way turns a pilot into a production system with a weekly report on accuracy, escalations and time saved — the way AI automation agencies should be judged.
The run path of one inbound request handled by an AI agent. Each request travels it on its own; anything uncertain, financial or legal stops and goes to a person.
A new email in the shared inbox, a form, a chat message or an uploaded document starts a run.
A language model works out what the request is — order status, invoice, contract question, complaint, lead — and how urgent it is.
Pulls the structured fields — names, numbers, dates, amounts — from the email or the attached PDF into a strict schema; OCR first for scans.
Low-confidence reads and unknown intents stop here and go to a person with the draft already filled in.
Reads the customer, order or deal from HubSpot, Salesforce or your ERP and searches the knowledge base — RAG over Drive, Notion or SharePoint.
The agent chooses from allowed tools only — create a task, update a record, draft a reply, schedule a call — within the rules you set.
Creates or updates the CRM or ERP record through the API — no screen clicks, no fragile bots.
Composes the answer with the facts pulled from your systems and the sources it used.
Anything that refunds, commits, signs or quotes goes to an approver in Slack or Teams first.
Sends the approved reply through Gmail or Outlook, or hands the thread to a person with the full summary.
Stores inputs, prompts, tool calls and outputs for audit, with the cost of each run.
Corrections made by people are collected weekly into examples and rules, so the agent stops repeating the same mistakes.
Accuracy, escalation rate, token spend and time saved, sent to the owner.
Typical minutes per inbound request — an email with a document, a form, a support question — for a small operations team. Your own numbers go into the calculator below.
Based on 18 min by hand and 2 min with the flow per item, from the table above.
The agent reads, decides within its rules, acts through APIs and logs everything. People keep the decisions with consequences.
Refunds, quotes, contracts and commitments are always approved by a person.
Emotional, conflicting or unusual situations are handed over with the context, not answered by the agent.
When the model is unsure, the draft goes to a person instead of out the door.
Which tools the agent may use, what it may say and where it must stop — written with you.
Regular review of runs, errors and costs by someone on your side.
How the team’s work changes is your decision; the agent takes tasks, not jobs.
Flip a switch to hand a step to the flow or take it back.
Benchmarks from Gartner’s June 2025 agentic AI forecast and the MIT NANDA report on generative AI in business: the gap between a pilot and a production agent is measurement, guardrails and a feedback loop.
We map the process as it runs today, count the minutes and agree what the flow must never do on its own.
A working flow on your real data, in a sandbox. You see every run and every exception.
Edge cases, approvals and alerts, then the switch-over — with the old way kept as a fallback.
Monitoring, fixes when a vendor changes a format, and a monthly report of hours saved.
An integrator connects systems; an RPA contractor builds bots that imitate clicks on a screen. We connect systems through their APIs and add a language model where a person used to read, classify or write — artificial intelligence and robotic process automation combined, without the fragile screen bots.
A chatbot answers questions. An agent reads a request, looks up your systems, chooses an action from the tools it is allowed to use — update a record, create a task, draft a reply — and hands anything risky to a person. It works inside your processes, not in a chat window.
Inbound email triage, document data extraction, first-line support with escalation, lead qualification and follow-up, internal document assistants and scheduled reporting. All of them have a clear input, a clear output and a person on call for exceptions.
The model works inside a strict schema, every fact in a reply must come from your systems, low-confidence outputs go to a person, and money and legal actions always need approval. Every run is logged, and corrections feed back into the agent’s examples.
The agent runs on your server or in your cloud account. API calls to the model providers are made under business terms that exclude training on your data, and for sensitive work the model can run locally so nothing leaves your network.
We start with one process, agree the metrics before building — accuracy, escalation rate, time saved — and ship to production in weeks, with a weekly quality report. A pilot that cannot be measured is not started.
You do: the n8n workflows, Python code and prompts are yours. Running costs are model tokens and hosting, both visible in the run log, plus optional maintenance when APIs or requirements change.
Describe it in two sentences — we reply within a day with a workflow sketch.
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