We build AI agents
that work in production.
Not demos. Not prototypes. Production agents running in real business workflows — qualifying leads, handling support, answering internal questions, processing documents. Fixed scope. Fixed price. In production in 4–8 weeks.
An AI agent is software that perceives inputs, reasons about them, and takes actions — without a human in the loop for every step. It's not a chatbot you talk to. It's not a dashboard that shows you AI insights. It's a piece of your operations that runs autonomously, 24/7, handling a defined set of tasks so your team doesn't have to.
The hard part isn't the AI model. Any team can call an API and get a response. The hard part is what surrounds it: the integrations that give it access to your real data, the evaluation framework that tells you whether it's actually performing, the guardrails that prevent it from doing something expensive or embarrassing, and the monitoring that catches problems before your team does.
That's what we build. 12+ years of production software engineering behind each one. The agent handles the routine. Your team handles the rest.
- Software connected to your real systems
- Autonomous on routine tasks, 24/7
- Designed to escalate to humans when needed
- Monitored, evaluated, and improvable
- A generic chatbot bolted to your website
- An AI strategy deck with no implementation
- A tool that requires constant supervision
- Something you "try" for a week and forget
Four agents.
Each one ships.
Productized builds for the highest-ROI use cases we see repeatedly. Each one starts from a proven blueprint — customized to your stack, your data, and your team.
Every inbound lead gets qualified, scored by intent and fit, and routed instantly — whether it arrives at 2pm on a Tuesday or 11pm on a Sunday. Your sales team only sees the warm ones, with context already assembled. Nobody goes cold while the team sleeps.
This is the highest-impact starting point for most sales-led businesses. SDRs stop drowning in unqualified noise. High-intent leads stop waiting 18 hours for a first response.
What it does
- Ingests new leads from forms, ads, demo requests, and inbound email in real time
- Enriches each with firmographics, tech stack, and intent signals
- Scores against your ICP rubric, which we build with you during kickoff
- Delivers qualified briefs to your sales team via Slack, CRM, or email
- Books meetings directly for high-intent leads via Calendly or Cal.com
- Logs every decision back to the CRM with reasoning, so the team can audit and improve it
What you get out of it
- Zero leads go stale because the team was offline or overwhelmed
- SDR time focused on conversations, not triage
- A qualification rubric that forces clarity on what "good" actually means
- Audit trail of every scoring decision, editable as your ICP evolves
The real estate lead qualification agent we built for aynuraaghenii.com runs 24/7, qualifies inbound property enquiries, and scores leads by buying intent and timeline before a human ever touches them. The agent handles the overnight and weekend volume — the hours when leads used to go cold.
Your support team's time is too expensive for repetitive ticket answers. This agent reads every incoming ticket, finds the right answer from your help docs, policies, and customer history, and either resolves it autonomously or drafts a reply for an agent to approve in one click. Humans only get pulled in when the situation genuinely requires them.
Most clients see 40–60% support load reduction within the first 60 days. CSAT tends to improve — because first response times drop from hours to seconds.
What it does
- RAG over your help center, past resolved tickets, and per-customer account and order history
- Auto-resolves tier-1 tickets when confidence is high — order status, policy questions, basic how-tos
- Drafts replies for tier-2 tickets with sources and confidence score; agent approves or edits
- Escalates to the right human team with full context, not a blank ticket
- Learns from agent edits in the first 30 days to calibrate to your tone and standards
- Tracks cost and accuracy per response so you can see exactly what you're getting
What you get out of it
- Support volume scales without headcount scaling with it
- New support hires become productive faster — the agent handles the cases they'd have to look up
- SLA compliance improves across the board
- Your best agents spend time on complex, relationship-critical cases
The same questions get asked over and over in every growing team — to the same senior people who have other things to do. This agent indexes your documentation, SOPs, wikis, and Slack history, and answers any internal question with a cited source. New hires onboard faster. Senior staff stop being interrupted with questions a document already answers.
Knowledge is the most immediate ROI agent for teams that have been growing faster than their documentation practices.
What it does
- Indexes Notion, Confluence, Google Drive, SharePoint, Slack archives, and internal wikis
- Answers questions in Slack or a custom internal interface — with a cited source every time
- Identifies knowledge gaps: questions it couldn't answer, surfaced weekly to whoever owns the docs
- Refreshes its index on a schedule with delta updates as your documentation changes
- Handles follow-up questions in context, not just one-shot lookups
What you get out of it
- New hire onboarding time measurably shorter — they self-serve instead of interrupting
- Senior staff recover time from recurring internal questions
- Knowledge gaps get surfaced and fixed instead of quietly festering
- Institutional knowledge stops being locked in the heads of three people
Contracts, invoices, reports, compliance documents — reading them takes hours. This agent reads them instead. It extracts the key data, flags anomalies against your standards, highlights deviations from your policy positions, and delivers a structured brief so the human reviewer focuses on decisions, not reading.
Built with a full audit trail — every extraction and flag is traceable, which matters when the documents are legal or financial.
What it does
- Trained on your past documents, redlines, and policy positions during kickoff
- Reads each incoming document and highlights deviations from your standards
- Extracts key data fields — parties, dates, amounts, obligations, termination clauses — into structured output
- Flags anomalies and non-standard terms with specific references to the source text
- Drafts a first-pass redline or summary a reviewer can edit and approve
- Maintains a full audit trail — every output is traceable to the source passage
What you get out of it
- Hours of reading compressed to minutes of reviewing flagged items
- Consistent application of your policy standards across every document
- Junior reviewers work on more documents with better accuracy
- Backlog of unreviewed documents becomes workable
Two steps.
No surprises in between.
Every AI agent engagement starts with an Audit. Builds only happen if the Audit says they should. You're never committed to a build you haven't scoped and approved.
The most common situation we see: you're certain AI should be doing more in the business, but you're not sure what to build, what it actually costs, or whether to build vs. buy something off the shelf.
In two weeks, we map your workflows, identify the highest-ROI agent candidates, and give you a ranked roadmap with rough scope, integration paths, and build-or-buy guidance for each. The output is a document you own — whether or not you build with us.
What you get
- A workflow audit of 2–4 functions you nominate: sales, support, ops, finance, etc.
- A ranked map of the 3–5 highest-ROI agent opportunities with estimated impact for each
- Architecture sketches for the top 2 — enough to scope a build or brief an internal team
- Build-or-buy guidance, including when an existing tool is the right answer
- A kickoff scope ready to green-light if you want to move forward
- A 60-minute readout with your leadership team
How it runs
- Week 1: discovery interviews with the people doing the work today, not just leadership
- Week 2: synthesis, impact modeling, and the deliverable
- About 4–6 hours of your team's time across the two weeks
What it isn't
- A strategy deck. The output is short, specific, and operational.
- A sales pitch for a build. About 40% of Audits end with us recommending an off-the-shelf product, an internal hire, or doing nothing yet.
We build, test, deploy, and document. The agent is handed over production-ready — with monitoring dashboards, an evaluation set tuned to your real data and edge cases, a runbook for whoever maintains it, and 30 days of post-launch tuning included.
Fixed scope and fixed price means you know exactly what you're getting and what it costs before we start. No hourly billing. No change orders for anything we agreed to deliver.
What you get
- A production-deployed agent running in your stack, not a demo environment
- Integrations with your existing tools — CRM, helpdesk, data warehouse, document store
- An evaluation set built from your real data and edge cases, not synthetic examples
- Monitoring dashboards: latency, cost, accuracy, escalation rate
- Full documentation and team training — handoff-ready from day one
- 30 days of post-launch tuning and bug fixes
How it runs
- Week 1: discovery, data access, eval set definition — no ambiguity about what success looks like
- Weeks 2–4: build and iterate against the eval set
- Weeks 4–6: shadow run with your team, refine against real cases
- Weeks 6–8: production launch, monitoring, handoff
We've learned to say no to certain categories of work — not because we can't do them technically, but because they consistently don't produce the outcomes clients actually need.
- AI strategy decks with no implementation path. We build things. If you want a report, there are consultancies for that.
- Generic AI training programmes. Teaching your team to use ChatGPT is not the same as deploying a production agent. We do the latter.
- Off-the-shelf chatbot deployments. Connecting Intercom's AI to your website and calling it an agent isn't what we do. We build custom agents for custom workflows.
- Projects we don't think will move the needle. If after the Audit we don't believe a build will achieve the ROI you need, we'll tell you clearly and you walk away with a useful output either way.
- Unlimited-scope retainers. Every engagement is scoped. Every deliverable is agreed in writing. No open-ended billing.
Most AI companies were born in the LLM era. We weren't. Aggento was built on 12+ years of production software engineering — systems that had to work under real load, integrate with real legacy infrastructure, and be maintained by real teams long after the agency left.
That background is why our agents are different. The LLM isn't the hard part. Any developer can call an API. The hard part is the integration layer that gives the agent access to your real data, the evaluation framework that tells you whether it's actually performing, the guardrails that prevent costly errors, and the monitoring that catches regressions before they become incidents. That's production engineering applied to AI — and that's the only kind we do.
Agents in production today
The real estate lead qualification agent running on aynuraaghenii.com qualifies inbound property enquiries 24/7 — scoring leads by intent and buying timeline before a human reviews them. It's not a demo. It's live, handling real traffic, and producing qualified briefs for the sales process every day.
See the full case study library on the Work page.
Fixed scope.
Fixed price. No hourly billing.
AI Opportunity Audit
Maps your workflows, ranks agent candidates by ROI, delivers a prioritised roadmap. Output is a document you own.
Agent Pod Build
Build, test, deploy, document. Handed over production-ready with monitoring, documentation, and 30 days post-launch support.
Third-party costs — model API tokens, vector database hosting, cloud infrastructure — are passed through at cost and estimated transparently before the build starts. They're typically £200–800/month for a production Pod at typical usage volumes.
What buyers always ask.
We use whatever model is right for the job — Claude (Anthropic), GPT-4o (OpenAI), and open-weight models for on-prem deployments where data can't leave your infrastructure. We're not tied to a single provider and we don't take referral fees from any of them. The model is one component of a production agent. What actually determines quality is the architecture around it: how retrieval works, how the agent is evaluated, what guardrails are in place, and how it's monitored. We select and justify the model selection during the Audit or kickoff scoping.
The Audit takes 2 weeks. Agent Pod builds take 4–8 weeks from kickoff to production. The specific timeline depends on integration complexity — a knowledge agent connected to Notion and Slack is faster than a document review agent with a legal audit trail integrated into your DMS. We agree on the timeline in writing at kickoff before work starts. The evaluation set definition in week 1 is what makes fixed timelines possible — it eliminates scope ambiguity early.
Every Pod includes 30 days of post-launch tuning and bug fixes. After that, you can run the agent yourself — we hand over documented code, runbooks, an eval framework, and monitoring dashboards. Most clients are operating independently within 60 days. If you want ongoing support, we offer an optional retainer: monthly, cancel any time. About a third of clients take it; the rest prefer to run in-house. We build for handoff from the start — we'd rather you be self-sufficient than dependent.
Yes — and they need to in order to be useful. An agent that can't read your CRM, helpdesk, or document store is just a chatbot. We connect via your existing APIs, OAuth integrations, or direct database access, depending on your architecture. REST, GraphQL, SOAP, databases, on-prem systems, custom auth — we've worked with all of it. The Audit confirms integration feasibility and documents the data access approach before we scope the build. Security practices, ZDR configurations, and data handling are covered in writing before we start, and we sign mutual NDAs as standard.
Ready to scope
an agent?
Tell us which agent fits your situation — or describe your workflow and we'll recommend the right starting point. We respond within one business day with a scoping call or a recommendation to start with an Audit.
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