AI products, engineered for the real world.
We help founders, SaaS companies, and product teams turn AI opportunities into reliable, commercially viable products.
- AI products we design, build, and operate
- 3
- product, AI, and backend, no handoffs
- 1 team
- ownership from concept to production
- End to end
- to any single AI model or vendor
- No lock-in
AI products we design, build, and operate
product, AI, and backend, no handoffs
ownership from concept to production
to any single AI model or vendor
Products we built and operate
Owned products are the proof. Each runs as an independent business, with its own site, and doubles as evidence of how we engineer AI software.
LeadVector
AI sales intelligenceSales teams waste hours qualifying accounts by hand instead of talking to buyers who are ready.
For B2B revenue and sales teams
AnswerRank AI
Answer engine optimisationBrands lose visibility as buyers move from search results to AI-generated answers they cannot measure or influence.
For Marketing and content teams
AI-CMO
Autonomous marketing operationsEarly-stage teams need senior marketing judgement and execution long before they can hire for it.
For Founders and lean marketing teams
Six kinds of AI system, engineered to run in production
From complete AI-native products to the infrastructure underneath them, chosen for the problem, not the hype.
AI-native SaaS products
Complete products where AI is core to the experience, engineered for reliability and scale.
RAG and knowledge systems
Retrieval over private and domain data, with permissions, citations, and grounded answers.
AI agents and workflow automation
Multi-step workflows with tool use, human approval, and cost and reliability controls.
Voice and conversational AI
Inbound and outbound voice systems with real telephony, routing, and guardrails.
Backend and data platforms
APIs, pipelines, and multi-tenant infrastructure that AI products depend on.
Production AI infrastructure
Evaluation, monitoring, and provider resilience for systems running in production.
Client products, engineered and shipped
Client work is the proposition. Representative examples of the systems we design and build. Named case studies are published with client permission.
A grounded knowledge assistant for a revenue platform
Support and sales teams could not find answers across scattered docs, tickets, and product data.
Median answer time reduced from minutes to seconds across a high volume of monthly queries.
Distinctive. Graph and vector retrieval with strict permissions and inline citations for every claim.
An inbound voice agent for high-volume scheduling
Missed and abandoned calls were losing bookings during peak hours.
Automated resolution of most routine inbound calls, with clean handoff to staff.
Distinctive. Low-latency speech orchestration wired directly into the existing scheduling and CRM stack.
From MVP to a production-ready product in one quarter
A promising prototype had no evaluation, no monitoring, and no path to reliable scale.
Shipped a monitored, evaluated production system ready for the first paying cohort.
Distinctive. Evaluation harness and cost controls built in from the first architecture decision.
One operating model, from idea to production
A single, dependable path that carries a product from a defined problem to a monitored system running in production.
- 01
Define
Clarify the product, the user problem, the commercial case, and whether AI is the right fit.
- 02
Design
Create the product experience, system architecture, data model, and evaluation approach.
- 03
Build
Implement the product, backend, AI workflows, integrations, and infrastructure.
- 04
Operate
Monitor quality, latency, cost, reliability, and product adoption in production.
Product builders, not presentation consultants
We are engineers who ship and operate real software. That shapes every decision, from architecture to what we choose not to build.
Field notes from building AI products
Practical engineering and product thinking, not daily AI news.
Evaluating grounded answers in RAG systems
A practical approach to measuring whether a retrieval system is actually answering from its sources.
How we kept LeadVector's inference costs predictable
The routing, caching, and evaluation decisions that stopped model spend from scaling with usage.
Designing voice agents for latency people can live with
Where the seconds go in a voice pipeline, and the architecture choices that get them back.
Practical intelligence for people building with AI.
A concise briefing on important AI developments, product engineering lessons, emerging use cases, and what technical and product teams should do next.
Twice per month. No noise.
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