AI systems that reach production.
We design, build and operate AI that does real work inside your business — then stay accountable to the number it was supposed to move.
- Four
- Phases from problem to operated system
- Two weeks
- From first call to a working prototype
- 6–12 weeks
- To a production deployment
- You
- Own every line of code we write
Most AI projects die between the demo and the deployment.
The prototype works. Everyone is impressed. Then it meets real users, real data and a real budget, and it quietly stops being discussed.
It is rarely the model that fails. It is everything around the model: no evaluation, so nobody can prove it works. No guardrails, so nobody trusts it with a customer. No observability, so nobody can explain what it did. No cost control, so the invoice becomes an argument. No owner, so it decays.
Across the industry, human validation of AI output has risen from 22% to 63% in a year — the signature of a market moving from experiment to production, where being roughly right stops being good enough.
| A pilot | A production system | |
|---|---|---|
| Correctness | It looked right in the demo | Measured against a labelled test set |
| Failure | Undefined | Escalates to a human with context |
| Visibility | Someone’s console | Traced, logged, alerting |
| Cost | Unknown | Measured per run, budgeted |
| Change | Breaks silently | Regression suite on every update |
| Ownership | The person who built it | A named owner and a runbook |
Five services, one discipline
Engagements usually begin with a diagnostic and end with a system somebody operates. You can enter at any point.
AI Consulting & Strategy
Find the two or three processes where AI actually pays, model the return, and prove it with a working prototype — in two weeks.
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AI Agent Development
Agents that take real actions in real systems — with tool access, guardrails, escalation paths and an evaluation suite that proves they work.
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AI Process Automation
Automating the document-heavy, repetitive, judgement-light work that consumes your team — with AI where it earns its place and plain code where it does not.
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LLM & RAG Integration
Making your own data usable by a language model — retrieval that returns the right passage, answers grounded in sources, and measured accuracy.
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Managed AI Operations
Someone accountable for your AI system after launch — monitoring, evaluation, model migrations, cost control and a monthly report against the metric.
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The method is the product.
Diagnose, build, evaluate, operate. The same four phases on every engagement, whatever the industry — each one ending in something you can hold, and each one a point where you can stop.
Diagnose, build, evaluate, operate
Each phase ends in a deliverable and a decision.
Diagnose
We map the workflow, rank the opportunities and model the return using your numbers.
- Process mapping with the people doing the work
- Feasibility and data readiness
- ROI model
- Working prototype
Build
Fixed scope. The system, its integrations, its guardrails and its tests.
- Agent and workflow architecture
- Integration with your systems
- Guardrails and escalation
- Evaluation suite
Evaluate
Before anyone trusts it. Measured on real cases, against thresholds agreed in advance.
- Labelled test set from your real cases
- Accuracy, groundedness, escalation rate
- Load and cost testing
- Sign-off against thresholds
Operate
Monitoring, model migrations, cost optimisation and a report against the metric.
- Continuous evaluation
- Model updates and migrations
- Inference cost optimisation
- Incident response
We built and shipped our own AI product.
Aira is a production AI agent that books appointments over WhatsApp — handling real conversations, for real businesses, today.
It is not a demo. It is a conversation engine, a booking service with availability logic, calendar synchronisation, a reminder worker running on a queue, an owner dashboard, and the operational scaffolding that keeps all of it honest.
We point at it for one reason: it is the difference between a firm that talks about production systems and a firm that runs one. Every architectural decision we recommend to a client, we have already had to live with ourselves.
How this work actually goes
Architecture, economics and evaluation — written for the people who have to make the decision.
Why AI pilots fail (and what production actually requires)
A demo proves an idea is possible. Production proves it is reliable, affordable and owned. Here is the gap between them, itemised.
Read
What actually drives the cost of an AI agent
Build cost, running cost, and the cost nobody budgets for — and why architecture, not model choice, decides all three.
Read
How to evaluate an AI agent before you trust it with customers
Accuracy is not one number. A practical method for building an evaluation suite that catches the failures that matter to your business.
ReadThe method travels. The context does not.
We work across sectors, but we do not pretend a claims workflow and a returns workflow are the same problem.
Industry
Healthcare & clinics
Patient scheduling, records summarisation and claims workflows, with clinical safety boundaries.
Industry
Retail & e-commerce
Support that resolves rather than deflects, plus order, returns and catalogue automation.
Industry
Financial services
KYC and onboarding, document review, reconciliation and servicing, with full audit trails.
Industry
Logistics & supply chain
Shipment exceptions, shipping documents and dispatch coordination at freight volume.
Industry
Real estate & construction
Instant lead response, site-visit scheduling, document handling and project reporting.
Industry
Education & training
Admissions response, student and parent support, and administrative workload.
Industry
Professional services
Document review, research and knowledge retrieval for legal, accounting and advisory firms.
Where we fit — and where we don’t
| Strategy firm | Dev shop | In-house team | Relayworks AI | |
|---|---|---|---|---|
| Gives you a roadmap | Yes | No | Sometimes | Yes |
| Writes production code | No | Yes | Yes | Yes |
| Builds an evaluation suite | No | Rarely | Rarely | Always |
| Operates it after launch | No | No | Yes | Yes |
| Senior people on the actual work | Partly | Varies | Yes | Yes |
| Time to production | 6–12 months | 3–6 months | Varies | 2–4 months |
Enterprise engineering, mid-market access
Both founders work as engineers at IBM on production systems at enterprise scale. That discipline — evaluation, guardrails, observability, audit trails — is what we bring to companies not buying an enterprise engagement. More about us.
You own everything
Code, evaluation suites, infrastructure definitions. Contractual, not a courtesy. No proprietary platform to keep renting.
Fixed scope, not hours
Diagnostics and builds are quoted as a scope with a number agreed before we start. Overrun risk sits with us, not you.
We say no
Roughly one process in four is better fixed with plain code, a database index or a process change. We would rather tell you that than sell you a model.
Frequently asked
We are an applied AI consultancy. We design, build and operate AI systems inside other companies — agentic automation, LLM and retrieval integration, and process automation. We work across every industry, because the method is the same: find the process where AI pays, build it properly, prove it works, and keep it running.
Most agencies stop at the prototype. The difference is what happens after the demo works: evaluation suites, guardrails, observability, cost control, escalation design and someone accountable for the business metric six months later. That is the part that decides whether an AI project becomes infrastructure or a write-off — and it is the part we specialise in.
All of them. We have built for healthcare, retail and e-commerce, financial services, logistics, real estate, education and professional services. The domain changes; the engineering discipline does not. See industries for how the work differs in each.
With a two-week diagnostic. We map the process, rank the opportunities, model the return using your numbers, and build a working prototype of the best candidate. It is a fixed fee agreed before we start, and it is credited against a subsequent build. Most clients then move into a fixed-scope build of six to twelve weeks.
Fair question, and the honest answer is: judge the engineering, not the founding date. Both founders work as engineers at IBM on enterprise production systems — the discipline this site describes is what we do every day at scale. Relayworks is where we apply it to companies who could never justify an enterprise engagement. We have also shipped and operate our own production AI product, Aira. Relayworks is independent and not affiliated with or endorsed by IBM.
You do, outright — including the evaluation suite and infrastructure definitions. It is in the contract. We do not build on a proprietary platform that you have to keep renting from us.
No. We use commercial API tiers with training disabled, or self-hosted open-weight models on your own infrastructure where policy requires it. Details on our security page.
We are based in Coimbatore, Tamil Nadu, and we deliver across India and remotely for clients in the US, UK and the Middle East. Discovery work benefits from being in the room; the build does not.
Let’s find out what AI can actually do in your business.
A 30-minute call. We will tell you honestly whether there is a case worth building — and if there is not, we will say so.