Development · AI Agents
AI Agents Services
Most “agents” break on the second turn. We ship the parts that make them production-grade — tool-use, retrieval, guardrails, evals and observability — so they resolve tickets, book meetings and run workflows you can actually leave running.
- Takes real actions via typed tools & MCP
- Guardrails + human handoff before it guesses
- Evals & tracing — measured, not vibes
user “where’s my order #4021 and can I still return it?”
plan plan: look up order → check return window → answer
▸ tool lookup_order(id=4021)
▸ rag retrieve: returns_policy.md
agent Order #4021 shipped Tue, arrives Fri. Returns are open for 22 more days — want a label?
What does REO Rank's AI Agents service build?
REO Rank builds production-grade AI agents — support, SDR, RAG and voice — that take real actions through typed tools and MCP, shipped with retrieval, guardrails, evals and observability so they run unattended. The same team owns your technical SEO, so the agents are built to be cited by AI search too.
- Support agents typically deflect 40–70% of ticket volume without dropping CSAT
- Model-agnostic: Claude, GPT or open models, chosen per task on cost and latency
- Guardrails plus confidence-gated human handoff — it hands off before it guesses
- You own everything: the code, prompts, eval suite and infrastructure
What we build
Eight agents earning their keep.
Start with the one that hurts most. Each links to how we scope, build and ship it.
Custom AI Agents
Tool-use, function-calling, orchestration
Bespoke agents that reason over your data and take real actions in your stack via tool-use and function-calling.
How we build itCustomer Support Agents
Deflect tickets, keep the handoff clean
Support agents that resolve the repetitive tickets from your own docs and escalate the rest with full context.
How we build itSales & SDR Agents
Qualify, enrich, follow up — 24/7
Agents that qualify inbound, enrich leads, draft personalised follow-ups and book meetings straight into your calendar.
How we build itRAG Knowledge Assistants
Answers with citations, not hallucinations
Retrieval-augmented assistants over your knowledge base that answer with sources and admit when they don’t know.
How we build itVoice Agents
Real-time voice, low latency
Low-latency voice agents for inbound and outbound calls — booking, triage, qualification and support.
How we build itWorkflow Automation
Multi-step automations across your tools
Agents and pipelines that run multi-step work across your tools — triage, data entry, reporting, back-office ops.
How we build itLLM & MCP Integration
Wire LLMs into your product & stack
Integrate LLMs and tool-use into your product or internal tools — including MCP servers that expose your systems to agents.
How we build itAgent Evals & Monitoring
Guardrails, evals, observability
The safety layer: eval suites, guardrails, tracing and cost/latency monitoring that keep agents trustworthy in production.
How we build itUnder the hood
How an agent actually works.
Not a prompt in a box — a loop with tools, grounding and a safety layer around it.
- Requestticket · chat · call · trigger
- Agent reasonsplan → act → observe
- ToolsRAG · MCP · your APIs
- Guardrails + evalscheck · handoff · trace
- Action + answerlogged & measured
Do the math
What could one agent give back?
Drag the sliders to your reality. This is the conversation we start with — then we scope the real number and the guardrails against your actual volume.
Hours given back / month160
Estimated saving / year$53,760
Rough estimate at 60% automation. We scope the real number — and the guardrails — against your actual volume before you commit a dollar.
The boring parts we don’t skip
The difference between a demo and production.
Tool-use & MCP
Typed tool schemas and MCP servers so the agent can safely act in your systems.
Retrieval (RAG)
Tuned chunking, hybrid search and re-ranking so answers are grounded and cited.
Guardrails
Input/output checks, PII and jailbreak filters, confidence-gated human handoff.
Evals
Task + regression suites so a prompt change can’t silently break behaviour.
Observability
Full request tracing plus cost, latency and quality dashboards.
Model-agnostic
Claude, GPT or open models — chosen per task against cost and latency.
Where it pays off
Where teams put agents to work.
Concrete workflows we ship — not a feature list. Each starts as a single scoped agent and expands once the numbers hold.
Support that resolves
An agent that reads your order, returns and policy systems and closes tickets end-to-end — refunds, tracking, “where is my stuff” — handing off only the genuine edge cases. Teams typically deflect 40–70% of volume without dropping CSAT.
SDR & research agents
Agents that research an account, draft genuinely personalised outreach and keep your CRM clean — so reps spend their time in conversations, not tabs.
Knowledge (RAG) assistants
Grounded answers from your docs, wikis and tickets with citations — for a support team, a sales team, or the whole company — instead of pinging the one person who knows.
Ops & back-office automation
The tedious cross-system work — data entry, reconciliation, report generation, triage — handed to an agent that calls the same tools your team does, with a full audit trail.
Voice agents
Inbound and outbound voice that qualifies, schedules and answers — wired to the same tools and guardrails as your text agents.
AI-search visibility
Assistants and content structured to be cited by ChatGPT, Perplexity and Google’s AI answers — because the team that builds the agent also owns how you get found.
Why most agents fail
Four ways agents break — engineered out.
Every “AI agent” demos well. These are the reasons they don’t survive contact with real users, and what we build so yours does.
It breaks on the second turn
Most demos answer once and fall apart in a real conversation. We build explicit state, memory and turn-taking so the agent holds context across a whole thread — not just the opening prompt.
It makes things up
Ungrounded models invent policies, prices and order numbers. We ground every answer in retrieval with citations and gate low-confidence replies to a human, so it says “let me check” instead of guessing.
It can’t actually do anything
A bot that can only talk deflects nothing. We give agents typed tools and MCP so they read and write to your real systems — issue the refund, book the slot, update the record — inside your permissions.
You can’t tell if it’s working
Without evals and tracing, quality is a vibe. We ship regression suites, per-conversation traces and a cost-and-resolution dashboard, so you know exactly what it handled and what it cost.
Questions engineers ask
Before you scope a build
The things technical buyers check first.
Is this just a chatbot?
No. A chatbot answers questions; an agent takes actions — it calls your tools, reads and writes to your systems, and decides when to hand off to a human. We build the second thing, with the guardrails that make it safe to leave running.
Which models and providers do you use?
We’re model-agnostic. Claude, GPT and strong open models each win on different tasks; we pick per workload against accuracy, cost and latency, and abstract the provider so you’re never locked in.
How do you stop it hallucinating or going off the rails?
Grounding via retrieval with citations, input/output guardrails, a confidence threshold that hands off before it guesses, and an eval suite with regression gates so behaviour is measured, not assumed.
How long until something is live?
Weeks, not months — because we start with one well-scoped workflow, ship it behind guardrails, prove the numbers, then expand. You see a working agent on your data early, not a slide deck.
Do you host it, or do we?
Either. We can deploy into your cloud and boundary (so data never leaves) or run it for you — both come with the same tracing, evals and cost monitoring.
Why an SEO agency for AI agents?
Because we’re engineers who also own how you get found. The assistants we build are structured to be cited by AI search, and the same team that recommends your technical SEO ships it. Most agencies can only do one half.
What does it cost to run in production?
Running cost is model tokens plus hosting, and it’s usually a fraction of the labour it replaces. We design for it — routing simple turns to cheaper models, caching retrieval, and showing cost-per-resolution on a live dashboard so the economics are never a mystery.
Will it integrate with our existing tools and stack?
Yes — that’s the point of an agent over a chatbot. We give it typed tool schemas and MCP servers that call your real systems (CRM, order DB, helpdesk, internal APIs), so it acts inside the stack you already run rather than a walled garden.
Is our data used to train models?
No. We deploy inside your cloud and trust boundary where you need it, use providers on no-training terms, and keep retrieval data in systems you control. Your data trains nothing.
What do we own at the end?
Everything — the code, the prompts, the eval suite and the infrastructure. No proprietary black box you can’t inspect or leave. If you ever part ways, the agent keeps running.
Still have questions? Talk to a specialist
The team
The senior specialists behind your work.
No account-manager buffer and no juniors learning on your budget — you work directly with the people who do the work.
Rao Usama
Co-Founder & Product Officer, Head of AI / Data
14+ yrs engineering · AI agents, MCP & automation
Nimra Jabbar
Front-End Developer & SEO Specialist
MSc Data Science · front-end · technical SEO
Abdur Rahman Shah
Web & SaaS Engineer · DevOps
WordPress · custom web apps · SaaS · DevOps · technical SEO