Architecturally sound, cost-governed AI systems engineered with the same discipline behind global payment networks. We bring engineering discipline to AI implementation — focusing on reliable systems and measurable outcomes.
No obligation · Architecture-led · Senior engineering only
The Problem
In the rush to launch AI, most teams ship prototypes that demo well — and then quietly bleed money, performance, and trust as soon as real usage arrives.
Unoptimised API calls and chatty agents that quietly eat your margins as soon as real usage arrives.
Brittle prompts and undocumented chains that break with every model update — and nobody knows why.
Zero governance over how proprietary data flows into LLMs, vector stores, and third-party providers.
We don’t just build AI. We engineer it.
The Solution
We don’t start with the LLM; we start with the foundation. Our hybrid UK–India team ensures your AI is secure, performant, and profitable.
01
Stop the bleeding before it reaches production scale. We implement semantic caching, token optimisation, and intelligent request routing so your inference cost grows linearly with value — not with traffic.
02
We treat AI like software, not magic. Robust prompt versioning, regression testing, evals, and multi-model failovers so your system stays reliable through every model update.
03
Your data is your moat. We build secure RAG pipelines with strict data boundaries, audited retrieval, and policy-aware responses — so your IP stays yours and your answers stay accurate.
The Proof
The pattern we see again and again: a promising AI prototype meets real-world traffic, costs spiral, and the team can’t safely ship the next change. Architecture is the unlock.
The Challenge
AI feature was working — but cost-per-request was climbing faster than usage, and every prompt change risked a regression nobody could detect.
The Fix
We re-architected the request path with a custom semantic caching layer, intelligent model routing, and an evals harness so prompt changes ship safely.
The Result
A material reduction in inference spend, predictable unit economics, and a system architected to absorb the next order of magnitude of growth.
Engineering Leadership
Engineering Leadership Team
Architecture, governance and reliability
Practical guidance for production AI
We approach AI implementation through architecture reviews, cost governance, testing and operational readiness. These practices help teams manage technical debt and build reliable production systems.
— Aneto Digital Engineering Leadership
Don’t let your AI bill be your biggest surprise next month. Let’s look at your architecture and find the leaks — together, in 15 minutes.
Book Your 15-Minute AI Architecture AuditSenior-led · No obligation · Architecture-first