>_ Boutique AI Consultancy

Purpose-built agents

for answering data questions

Think beyond UIs and chatbots. We build intelligent AI systems on top of your existing tools and processes.

>_ Trusted By

>_ Agents

What we build

Isometric illustration of data agent infrastructure

>_ 01

Data Agents

Query your operational data in plain language.

Natural language queries: Agents connected to your databases, warehouses, and internal systems. They return structured answers and summaries rather than raw query results.

Agentic data infrastructure: Data infrastructure built so agents can read from your sources, reason across them, and trigger actions in other systems.

Observability and evals: Every agent response logged, scored, and traceable, so you can trust what the agent says and catch regressions early.

Isometric illustration of workflow automation

>_ 02

Workflow Agents

Automate the processes your team has outgrown.

Trigger-based automation: Agents that watch for events across your tools and handle the response: routing, filing, notifying, or escalating, all without manual input.

Human-in-the-loop design: For decisions that require judgment, we build structured review steps so automation handles the volume and humans handle the edge cases.

Legacy process replacement: We trace the email chains, spreadsheets, and manual handoffs that are holding your team back and replace them with durable agent workflows.

Isometric illustration of a support agent

>_ 03

Support Agents

Handle inbound queries without growing your support team.

Triage and routing: Classifies and routes support requests to the right team, or resolves them outright, based on your playbooks and knowledge base.

Escalation logic: Clean handoff to a human with full context when the agent reaches its limits. Judgment calls stay with people.

Knowledge base grounding: Grounded in your documentation, past tickets, and product data so answers are accurate and consistent.

>_ About Our Founders

Founder-led by experienced operators

John Reid

0→1 AI agent infrastructure product (Signals by Snowplow.io) conception, development & delivery

10+ years of product & data expertise @ VC-backed startups & scale-ups in Southeast Asia and Europe

Launched four 6-figure ARR products (Enterprise). Defined & implemented growth strategies for luxury e-hailing business in 4 markets across a 100m+ GMV business (Consumer)

James Reid

0->1 operational product experience building Iceberg, an integrated contract, vendor and billing management platform

Operations and data engineering lead at a Singaporean insurtech during hyper-growth period

CFA, MBA, 4+ years M&A advisory at Rand Merchant Bank

>_ Process

How we work

>_ 01

Diagnose

Find where an agent actually adds value.

Not every workflow needs an agent. We start by mapping how work moves through the business: what triggers it, which teams touch it, where judgment calls happen, and where repetition is burning time.

We're looking for the places where an agent can act reliably: structured inputs, clear success criteria, and data that already exists somewhere in your systems. The diagnosis shapes everything that follows.

>_ 02

Design

Design the agent before writing a line of code.

What data sources does the agent need to read? What tools can it call, and under what conditions? Where must a human stay in the loop, and what does a wrong answer cost?

We define the agent's scope tightly. A narrow agent that works reliably beats an ambitious one that hallucinates. We specify the integration points, access controls, and evaluation criteria before the build starts, so there are no surprises when it meets real data.

>_ 03

Prototype

A working agent in front of real users within the first week.

We move quickly to a live environment connected to your actual data, not a demo setup. Real users interact with the agent while it’s still being shaped. Prompts evolve. Tool definitions get refined. Edge cases appear early, while they’re cheap to fix.

Fast iteration means less time debating what the agent should do and more time watching what it actually does when people use it.

>_ 04

Engineer

Production agents need more than accuracy.

As the agent stabilises under real usage, we harden it. Structured evals so regressions are caught before they reach users. Observability so you can see what the agent is doing and why. Rate limits, RBAC, and audit trails so the right people access the right context.

We set up dev, staging, and production environments and work across the full stack: application layer, infrastructure, and CI/CD, tightening constraints as more depends on it.

>_ 05

Harden & Transfer

A system your team can run and extend without us.

Before release, we run structured security testing — penetration testing, automated checks in the CI/CD pipeline — and operate the agent initially to make sure it holds up under real load with real users. We monitor response quality, catch edge cases that weren't visible in testing, and tune as needed.

Over time, ownership transfers to your team. We work inside your repository and environments with standard contributor access. The goal is an agent your team understands, can monitor, and can extend without us.

>_ 01

Diagnose

Find where an agent actually adds value.

Not every workflow needs an agent. We start by mapping how work moves through the business: what triggers it, which teams touch it, where judgment calls happen, and where repetition is burning time.

>_ Testimonials

Hear from our customers

What impressed us most was how quickly Reidworks got to the real problems — they spoke to everyone from our engineers to our operations team, and that showed in what they built.We came away with usable tools and a much clearer sense of where AI can move the needle for us.

Chief Innovation Officer

Payments Company

They handled the full stack — optimised our Postgres layer, added proper tracing and evals, and set up monitoring so we could trust it in production.The result is a data agent that's accurate, fast, observable, and delivering consistent analysis across our customer base.

CEO

FMCG IoT Company

>_ Let's Talk

Keen to connect?

Whether you have a workflow you want to automate, data your team can't easily query, or a product feature that could benefit from an agent, we'd like to hear about it.

View Our Work