An agent isn't a chatbot. It's a digital colleague with a clearly defined job.
We build agents that read documents, act on your rules and hand judgment calls to people. Here is how we define their role, inputs, outputs and guardrails.
What an AI agent actually is — and how it differs from ordinary automation
Automation follows a fixed rule. An agent works toward a goal using context, then escalates anything outside its limits. That is the difference between a script and a digital colleague.
Automation = a fixed track
A fixed procedure handles predictable input reliably and cheaply, but stalls on exceptions.
Agent = a goal plus judgment
Ask it to create ERP line items and it can interpret differently written orders because it understands content, not only format.
Why it matters to a decision-maker
Agents can take on document-heavy routine that once required human interpretation — where time and know-how leak today.
It's not magic, it's engineering
Production reliability comes from a defined role, rules, integrations and metrics. Without them, an agent remains a demo.
What a reliable agent is made of — step by step
An agent is a system of defined components, not one “smart model”. Each component below is needed for dependable long-term operation.
Role — what it does and what it does NOT
We map the process, then define the agent's job and its boundaries. Clear limits are central to reliability.
Input and output at every step
Every PDF, email or query has an explicit output such as ERP rows, a draft reply or an import file. That makes results testable and measurable.
Rules and guardrails — may / may not
We specify what the agent may do alone and where it must stop. Those guardrails make access to live systems trustworthy.
Tools and integrations — the agent's hands
We connect ERP, SharePoint, M365, CRM, PDM/CAD, email and WhatsApp through APIs or RPA, supported by a central data layer.
Memory and context — RAG
We consolidate company documents, run scanned files through OCR and let the agent answer from approved context — always with a source.
Human-in-the-loop — where the person decides
The agent recommends; a person approves. We define each handover point so judgment and sign-off remain human.
Metrics — what it's evaluated by
We track errors, response time, saved work and adoption against a baseline, then tune the agent in production.
Governance and security from the start
Ownership, access and AI-use rules are designed upfront. Sensitive workloads can run privately on your own infrastructure.
What the agent does not do: send data externally without approval, delete data, make financial decisions or train for anyone else on your data.
The technology under the hood
What our agents can do in practice
Each type below has been delivered in a real project. We select the model and tool for the task and data sensitivity.
Reading / extraction agent
Turns drawings, bills of materials, invoices and contracts into structured data.
Email agent
Extracts order lines into ERP and drafts replies to recurring queries.
Knowledge / RAG agent
Answers from company documentation with sources, keeping know-how inside the business.
Process agent
Turns meeting transcripts into tasks and escalations that teams can run themselves.
Reporting agent
Answers natural-language questions over ERP or warehouse data.
Communication agent / chatbot
Handles 24/7 web or WhatsApp inquiries and escalates the rest.
How we build an agent you can trust
A trustworthy agent knows when information is insufficient and hands the decision to a person. We design that boundary before deployment.
Always an answer with a source
RAG answers show the price list, drawing or document used, so users can verify them.
Human review where OCR falls short
We read reliable fields such as title blocks and bills of materials, while people complete ambiguous drawing details.
A document audit before quoting
We inspect 5–10 real PDFs first because layouts determine reliability, scope and price.
People make decisions; the agent handles routine
The agent handles transcription and creation; judgment and approval stay with people — essential in regulated work.
What we measure and how we operate agents long term
After go-live, we evaluate and tune every agent against its baseline and business case.
What every agent measures
Response and process time, errors, rework, saved capacity and adoption — all against a measured baseline.
Typical payback of 3–12 months
We target returns that fund further development, not a one-off demo.
Operation and tuning, not handover and goodbye
We monitor errors, refine rules and expand a verified pilot where it pays.
The data stays with the company
Encrypted transfer and storage, GDPR controls and on-prem options protect sensitive data from day one.
A production-engineering agent in ERP Helios — an engineering and design firm
The pain we were solving
Process engineers manually created 70–120 similar Helios items each week from PDF, DWG and DXF files, including structures, drawings, routings and time standards.
An entry audit instead of promises
We reviewed 5–10 real drawings before quoting, then limited the pilot to one customer with a consistent title block.
A deliberate decision on what NOT to entrust to the agent
OCR reads the reliable title block and bill of materials, while complex dimensions stay with the engineer.
What the agent does on its own
It reads the files, finds similar ERP items and creates the component structure with inherited routing and time standards.
Human-in-the-loop for what only a person can see
A PDF preview asks the engineer for ambiguous values such as bends or sheet thickness; verified metadata then enters Helios by CSV import.
Integration and phasing
Helios connects through its API. Phase one covers manufactured items; phase two adds purchased and standardized parts, with quoting as a later option.
The result
The agent creates structures and carries over standards across 70–120 items a week; the engineer checks only what requires judgment.
Got a process that eats up your people's time?
Tell us which tasks keep repeating in your company. We'll design a solution and calculate the business case.