Real-World AI Agent Case Studies (Anonymised)

Most businesses don’t need “AI.” They need fewer dropped enquiries, faster resolution, fewer mistakes, and a clear audit trail when something goes wrong.

The examples below are anonymised. They include only information we are allowed to share and are deliberately written at a level that protects client identity, internal systems, and commercial details. We operate under strict confidentiality agreements and we treat client information as protected business data.

Case Study 1 — The “Inbox Collapse” Problem (Professional Services)

The situation
A small professional services firm was receiving enquiries via email, website forms, and social media. Messages were handled inconsistently across staff, leading to missed follow-ups and variable quality.

What we built
A triage agent that:

classifies inbound messages (sales/support/admin),
extracts key fields (intent, urgency, deadlines, contact details if present),
flags missing information and proposes questions,
produces an approved draft reply aligned to tone rules,
escalates risk cases (complaints, legal threats, sensitive data).

Guardrails (non-negotiables)

No commitments on pricing or timelines without human approval.
Escalate any complaint/refund threat.
Refuse and redact sensitive personal data.

Result (operational impact)
Staff replies became consistent, faster, and safer. The firm reduced “back-and-forth” clarification loops and created a reliable process for escalation and sign-off.

What made it work
The win wasn’t the model. It was the rules, the templates, and the acceptance tests that made the output predictable.

Case Study 2 — Wholesale Order Enquiries (Agri-food / Logistics)

The situation
Inbound messages often arrived as short, messy notes: product, quantities, delivery windows, and special constraints, sometimes missing key details. Human operators spent time extracting information and chasing clarifications.

What we built
A workflow-driven agent that:

extracts order intent and the minimum viable order fields,
identifies missing essentials (delivery date, address, packaging, substitutions),
generates a short, structured clarification message,
routes to the right human team when thresholds are met (volume, urgency, special constraints).

Guardrails

Never confirm stock availability as “guaranteed” unless explicitly verified.
Never invent delivery dates.
Escalate any message implying non-payment risk or dispute.

Result (operational impact)
Operators dealt with cleaner information earlier. The “first reply” became structured, consistent, and actionable, while reducing avoidable phone calls.

Case Study 3 — Appointment Requests (Local Services)

The situation
Bookings arrived as informal messages: “tomorrow morning?”, “after work?”, “same as last time”. The business was losing bookings due to slow replies and unclear confirmation.

What we built
A scheduling-focused agent that:

interprets the request and proposes the next step,
asks for the missing essentials (service type, preferred day/time, location, timezone),
drafts a confirmation message in a consistent tone,
escalates edge cases (refund demands, abusive language, vulnerable individuals).

Guardrails

Never confirm an appointment without a verified slot.
Always confirm timezone and service requirements.
Escalate complaint/refund threats.

Result (operational impact)
Less chaos, fewer misunderstandings, and clearer handover notes when a human needed to step in.

Case Study 4 — “Support Triage” for a Digital Product

The situation
Support emails contained mixed issues: account access, billing, bugs, and misunderstanding of how a feature worked. Replies were uneven depending on who picked it up.

What we built
A support triage agent that:

classifies the issue type,
suggests the next diagnostic questions (browser/device/steps/errors),
drafts a helpful reply using approved templates,
flags likely escalation conditions (billing disputes, repeated failures, security concerns),
keeps an audit log of rules fired and why.

Guardrails

Never request passwords or sensitive information.
Escalate anything that looks like an account compromise.
Avoid promising fix timelines.

Result (operational impact)
Faster first response, better diagnostics, and fewer risky replies. Human support staff focused on real incidents rather than repetitive back-and-forth.

Case Study 5 — Food Transport Company (Temperature-Controlled Deliveries)

The situation
A food transport operator (multi-vehicle, time-sensitive routes) was drowning in mixed messages: delivery requests, last-minute changes, delays, proof-of-delivery queries, and occasional disputes. The same information was asked repeatedly: pickup window, delivery address, product type, temperature requirements, pallets, and contact details.

What we built
An operations triage agent that:

identifies whether the message is a new shipment request, a change request, a delay/incident, or billing/documentation;
extracts logistics-critical fields (collection/delivery windows, addresses, product type, temperature band, pallet count/weight, special handling);
generates a structured “missing info” checklist for the dispatcher to send back in one message;
produces a dispatcher-ready summary for internal handoff and audit trail.

Guardrails

Never promises ETA unless confirmed by operations.
Escalates immediately if: food safety risk, temperature breach, accident, or complaint/legal threat.
Never shares driver personal details beyond approved disclosure rules.

Result (operational impact)
Dispatchers spent less time interpreting messy notes and more time making decisions. Handoffs became cleaner, and incident escalation became consistent and documented.

Case Study 6 — Real Estate Agency (Lead Handling + Viewing Pipeline)

The situation
An estate agency received leads from multiple sources with incomplete info: “Is it still available?”, “Can I view tonight?”, “What’s the minimum contract?”, “I’m relocating next month”. Agents lost time qualifying leads and replying consistently, and viewings were sometimes booked with missing prerequisites.

What we built
A sales/lettings assistant agent that:

classifies incoming leads (buy/rent/sell/landlord enquiry) and urgency;
extracts key qualifiers (budget range, desired area, timeline, property type, number of bedrooms, financing status where voluntarily stated);
drafts responses using approved tone and compliance-safe language;
proposes next steps (qualifying call, viewing request, documents needed, suggested alternative listings without overpromising);
escalates sensitive or risky cases (discrimination cues, harassment, threats, or sensitive personal data).

Guardrails

No misleading claims (availability, pricing, legal terms) without confirmation.
No advice framed as legal/financial guidance.
Escalate anything that could become a complaint or reputational risk.

Result (operational impact)
Faster first response and better-qualified viewings. Staff stopped retyping the same explanations and focused on high-intent leads.

Case Study 7 — Multi-Country Farm Exporter (EU/UK/North America Shipments)

The situation
A farm exporting mixed fruit and vegetables faced complex enquiries: seasonal availability, specifications (sizes/grades), certifications, packaging, lead times, shipping terms, and compliance requirements by region. Enquiries often arrived as vague requests (“send your price list”) that couldn’t be answered safely without clarification.

What we built
A commercial + compliance triage agent that:

classifies enquiries (wholesale enquiry, certification request, logistics question, complaint, documentation request);
extracts core deal fields (product list, volumes, destination, preferred Incoterms where stated, delivery window, packaging, certification needs);
generates a precise clarification message to obtain missing requirements in one go;
drafts a compliant, non-committal holding response when availability/pricing depends on season and confirmation;
escalates any compliance risk or dispute to a human with a structured handoff note.

Guardrails

Never confirms pricing, stock, or delivery dates as final without human validation.
Never invent certifications or compliance statements.
Escalate: food safety concerns, regulatory demands, threatened disputes, payment risk signals.

Result (operational impact)
Enquiries became structured earlier, reducing cycles of back-and-forth. The business improved consistency in how it handled certifications and destination-specific requirements, while keeping humans firmly in control of commitments.

Why these projects succeed

An AI agent becomes useful when it is:

Bounded (clear scope and “must never do” rules),
Testable (acceptance tests and measurable pass/fail criteria),
Auditable (logs, rules fired, escalation notes),
Operational (someone owns updates, drift, and improvements).

That’s why we deliver agents via three stages:

Discovery Sprint (spec + guardrails + tests)
Implementation Sprint (build + operationalise)
Managed Ops (keep it reliable month after month)

You can also build internally after Discovery Sprint using your own team — the point is to give you a blueprint that is safe, specific, and implementable.

Want to see how it behaves?

We can share a private demo link to our reference triage agent so you can test it hands-on. Use the pre-sales enquiry form on our website to request access.