We connect AI to the systems a business already runs — CRM, ERP, helpdesk, document stores, internal tools — and automate the processes underneath. Agents that take actions, documents that read themselves, support that answers without a human, and internal assistants that know your company’s own data. Built with evaluation, access control and fallbacks, because that’s what separates a pilot from something operations can depend on.
Exelero integrates AI into existing business systems and automates the processes around them. Work includes AI agents that take actions across tools, document extraction and processing pipelines, customer support automation, internal assistants that answer from a company’s own documents and data, and integrations connecting AI to CRM, ERP, helpdesk, email, accounting and custom internal systems.
Every build includes the production layer most pilots skip: evaluation sets, access control, audit logging, human-in-the-loop review and fallbacks for when the model is wrong. Deployment can run on managed cloud or entirely inside a client’s own infrastructure. Engagements start with an automation audit and run as fixed-scope builds, dedicated teams or ongoing retainers.
This is the most common shape of an AI project. A demo works, leadership is enthusiastic, and then it stops — because the tool has no access to the systems where the real data lives, because nobody can prove it’s accurate enough to trust with customers, because security won’t approve sending internal documents to a third party, or because the process it automates has fourteen exceptions and the demo handled one.
None of those are model problems. They’re integration, evaluation, governance and process-design problems, and they’re most of the work. We start from them rather than reaching them in month four.
Agents that take real actions across systems, not just answer questions: creating records, moving tickets, drafting and sending, triggering workflows, escalating when confidence is low. Internal assistants that answer from company documents, policies and data with source citations. Tool and function layers so AI can reach your systems safely.
Extraction from invoices, contracts, forms, statements and scanned documents; classification and routing; validation against your business rules; structured output into the systems that need it; human review queues for the cases that warrant one.
Support deflection with accurate, sourced answers; ticket triage and routing; drafted replies for agents to approve; lead qualification and enrichment; multilingual handling.
Back-office workflows that currently run on copy-paste; approvals and exception handling; scheduled data movement and reconciliation; alerting on things that need a human.
CRM, ERP, helpdesk, email and calendar, accounting, HR systems, data warehouses and custom internal tools; API and webhook layers; SSO and permission mapping so the AI can only see what the requesting user can see.
Evaluation sets built from your real cases, regression testing when models or prompts change, cost modelling per run, latency budgets, audit logging, guardrails, and self-hosted deployment where data cannot leave your environment.
We build in 8–12 weeks from scope to launch, starting from foundations already built, with scope fixed in the first two weeks and one team across the whole stack. A single automated process typically lands inside the range, sometimes well inside it. What moves a build toward the upper end is the number of systems being integrated and the depth of the security review, not the AI work itself.
Two to three weeks. We map the candidate processes, estimate volume, error cost and time saved for each, and rank them by what’s actually worth automating. You get a prioritized list with effort and payback attached — including the items where our recommendation is to leave them alone. Yours whether or not we build.
One process, end to end, in production with real users and a measured before-and-after.
Defined scope, timeline and price. Built and handed over with source, infrastructure and documentation.
Our engineers inside your process.
Evaluation maintenance, model updates and cost optimization on a retainer. This matters more here than in conventional software, because the underlying models change whether or not you do.
Operations-heavy businesses where people spend their day moving data between systems. Support teams facing volume they can’t hire against. Finance and back-office functions processing documents by hand. Professional services firms with knowledge locked in files nobody can search. Companies that ran an AI pilot that never shipped. Businesses with sensitive data who need everything running in their own infrastructure.
8–12 weeks from scope to launch, and often less for a single process. The audit comes first, so you know what you’re building and what it’s worth before anything is committed.
With the processes, not the technology. The automation audit ranks your candidate processes by volume, error cost and time saved, so the first build is the one with the clearest payback rather than the one that demos best. Most companies find their best first candidate is duller than they expected — document handling and data entry, not customer-facing chat.
Yes, and that’s most of the work. We integrate with CRM, ERP, helpdesk, email, accounting, HR systems and custom internal tools through APIs, webhooks or direct database access, with permissions mapped so the AI can only see what the requesting user is allowed to see.
Self-hosted and on-premise deployment is standard for clients whose data can’t leave their environment, and it’s frequently the deciding factor in a security review. Where a hosted model is acceptable, we configure retention, regional processing and access controls to match your policy.
An evaluation set built from your real cases, with a measured accuracy rate before anything goes live, and regression tests that run whenever a prompt or model changes. We also set the confidence threshold below which work routes to a human instead of completing automatically — that threshold is a business decision, and we make it with you.
Cost per run is modelled during the audit, before you commit. It’s a design decision rather than a fixed property, and it typically moves by an order of magnitude between a naive implementation and a considered one.
It’s designed for. Confidence thresholds, human review queues, fallbacks to the existing manual path, and audit logs so any decision can be traced and explained. An automation people can’t override is one they’ll route around.
Yes. Source, infrastructure and documentation on handover.
Bring one workflow that eats your team’s week. We’ll tell you whether it’s automatable, what it would cost, and what it would save.