In short
An agent that only talks is a chatbot. We build agents that do work: they read the inbox, reconcile the invoices, update the CRM, and hand a human exactly the decisions that need one. This is for teams whose skilled people spend hours every week on knowledge work a machine can draft, route or complete.
Deliverables
What you get.
Everything listed here is a thing you can hold: a document, a system, a working piece of software. Not hours, not effort.
- 01Workflow scoping: we map the process and pick the steps an agent should own, and the ones it should never touch
- 02Agent design and build, wired into your existing systems: email, CRM, ticketing, documents
- 03Tool integrations with least-privilege permissions, so the agent can only reach what it needs
- 04An evaluation suite built from your real tasks, run before anything goes live
- 05Guardrails: escalation rules, approval checkpoints and audit logs on every action
- 06Handover documentation and a runbook your own team can operate
Capabilities
What we can take on.
The concrete jobs this service covers. Yours is not listed? Ask anyway: the honest answer is often a sibling service.
- Inbox and ticket triage that routes every message to the right place
- Invoice and document processing, from reading to reconciliation
- Research agents that come back with a briefing, not a pile of links
- CRM and data-entry agents that keep records current on their own
- Approval-gated agents that ask a human before anything irreversible
- Customer-facing assistants in more than one language
Process
How we work.
Four stages, one line. The order never changes; the depth does.
- 01Shadow the workflowWe watch how the work actually happens, not how the process document says it does.
- 02Prototype on real tasksThe agent runs against your historical workload, offline, where mistakes cost nothing.
- 03Prove itIt ships only when it beats the accuracy bar we agreed on in the evaluation suite.
- 04Supervise, then releaseHuman checkpoints start tight and loosen as the agent earns trust.
The working model
The harness, drawn.
Four stages, narrated, with captions. Sound is off until you ask for it.
Why Web Fortuners
Three reasons, checkable.
- 01
We run agents in production, not demos
Edge Legal's assistant triages real legal enquiries in two languages, on a live site, today.
- 02
Evals before enthusiasm
If the agent cannot prove itself on your data, we tell you not to build it, and that answer is free.
- 03
The people you brief are the people who build
There is no account layer between you and the engineer.
FAQ
Questions, answered.
The things people ask before they write to us. Yours is not here? Ask it directly.
We are model-agnostic. Claude, GPT and Gemini all show up in our work, and we benchmark the options against your actual tasks before choosing. The model is a component, not the product.
One person who owns the workflow and a sample of real work items, such as past tickets, emails or invoices. Real history is enough to prototype against; we do not need a data science team on your side.
No, and we will not scope one that tries. Agents take the repetitive volume so your people keep the judgment calls. If a workflow is pure judgment, we say so and advise against automating it.
Fixed scope, quoted in writing before work begins. You approve the scope before we bill anything, and the evaluation bar is part of that scope.
AI pillar
Agents that do, not chat.
Models put inside the workflow, with the harness, the evaluations and the human checkpoints that make them safe to run in production.
- 02Agent HarnessesOrchestration, tooling, evals and guardrails for agents at scale
- 03API Integration & ImplementationLLM APIs, MCP servers and system integration done right
- 04Knowledge SystemsRAG and vector search over your company knowledge
- 05AI Strategy & EvaluationAdoption roadmaps, evals and safety for teams going AI-first
- 06Workflow AutomationEnd-to-end business processes automated with AI in the loop
- 07Conversational AgentsChat and voice agents that answer, qualify and book, around the clock