AI Agents That Do Real Back-Office Work — An AI Co-Worker, Not a Chatbot.

The best fit is high-volume, repetitive work that involves reading and light judgment: triaging incoming emails and tickets, reading documents and forms (including scanned PDFs and free text), extracting and validating data, drafting responses for a human to approve, routing cases to the right person, and updating records in your existing systems. The pattern is “read, decide, act — with a human on anything consequential.” An agent won’t replace your team — it takes the repetitive volume off them and escalates the genuinely hard cases faster.

RPA (robotic process automation) follows rigid, pre-defined rules on clean, structured input — click here, copy that field, paste it there. It’s fast and reliable for deterministic steps, and useless the moment a case needs interpretation or the input arrives as unstructured text. An AI agent handles exactly that: it reads messy inputs, makes a judgment call, and decides what to do — including when to escalate to a human. In practice the two work together: the agent handles the ambiguous cases, and hands the deterministic steps to RPA or scripts. We map which is which as part of scoping.

Four things, in layers. First, grounding: the agent reasons from your actual documents and data (retrieval/RAG), not a model’s general memory — the biggest lever against hallucination. Second, human-in-the-loop: anything consequential passes a person or a confidence threshold before it’s committed. Third, guardrails and evals: input validation, output constraints, and a standing test suite that scores the agent on real cases with every change. Fourth, an audit trail: every input, decision, and action is logged and reviewable. We don’t ship an agent we can’t measure.

Yes — that’s the core of what we do. Agents act through your APIs, with scoped permissions, using tool/function calling and MCP to read and write in your ERP (e.g. Odoo), CRM, ticketing, EHR, and internal tools. For Eska, the agent was integrated directly into the client’s Odoo ERP through a Node.js layer. Connecting AI to existing platforms is a discipline in itself — see System Integration — and the agents run on an orchestration layer that routes, retries, and monitors every call.

It depends on scope and engagement model. A bounded pilot on a single workflow is usually a fixed-price engagement scoped to a defined deliverable; broader, multi-workflow programs run as a dedicated team or Time & Materials, because scope expands as the agent earns trust. The main cost drivers are how many systems the agent must integrate with, how messy your inputs are, your compliance and data-residency requirements, and how much oversight each action needs. We give you a firm estimate after a scoping call — and we’ll tell you honestly if a simpler tool would do the job. For the wider AI picture, see AI Development.

It can be — if it’s engineered for it, which is our default. In regulated workflows, we keep a human in the loop on anything that matters, ground the agent in approved data, constrain what actions it can take, and record a full audit trail of what it saw, decided, and did, plus who approved it. We bring HIPAA, GDPR, and KBV experience to that design. The point of the audit trail is precisely so “the AI did it” is never the answer — you can show exactly what happened and why.

Almost always a pilot. We pick one high-ROI workflow with a clear metric, build a narrow agent with human-in-the-loop from day one, and measure it against that metric. A narrow agent that reliably does one job earns the trust (and the data) to expand deliberately into a suite. It also de-risks the investment — you see real results on a real workflow before committing to a broader program.

Neither. We’re a custom-software and real-time engineering firm that builds and integrates AI agents into real business systems. We don’t train foundation models, and we don’t assemble no-code templates that break on your edge cases. We’re model-agnostic — OpenAI, Anthropic, Gemini, or open models, chosen per task — and our value is in the reliability, integration, and compliance engineering around the model. See AI Development for the full cluster.