Advanced AI Architecture
02 / 06 · AI engineering
The foundations under an AI product: how it finds the right information, how its steps run, and how it copes with real traffic. Built to be fast, reliable and affordable.
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Good for you if
You already use AI tools or models, but answers come back wrong, costs creep up, or everything slows down once real users arrive.
Not the right fit if
Nobody has decided yet what the system should actually do.
Case 01 / 01 · Manufacturing & Logistics
01 · Who they are
A global manufacturer with more than 5,000 staff.
The six decisions under an AI product that holds up.
- 01Find the right information
- 02Give every agent a stop
- 03Feed it the real volume
- 04Choose hosting on measured numbers
- 05Plan for the slow or broken part
- 06Watch cost, speed and drift
Find the right information
Design retrieval systems — chunking strategy, embedding choice, ranking — and build the evaluation set that proves retrieval quality rather than assuming it from a few good-looking results
Give every agent a stop
Architect multi-step agent systems with explicit stop conditions, so a loop fails loudly and cheaply instead of running up a bill silently
Feed it the real volume
Build the data pipelines and infrastructure that feed models at the volume and latency the product actually needs in production, not the volume of the demo
Choose hosting on measured numbers
Choose between hosted APIs, self-hosted models, and hybrid setups based on real cost and latency numbers we measure, not on vendor defaults
Plan for the slow or broken part
Design for graceful degradation: what the system does, specifically, when a dependency is slow, wrong, or unavailable
Watch cost, speed and drift
Set up observability built for AI systems specifically — token cost per request, latency per pipeline step, retrieval quality, and drift — not just server uptime
What Advanced AI Architecture covers.
- 01Search across your own documents
- 02Retrieval that returns the right thing
- 03Agents that know when to stop
- 04Evaluation harness
- 05Cost, speed and drift monitoring
Search across your own documents
People find the answer in seconds instead of hunting for it, and the answer comes from your material.
Retrieval that returns the right thing
The part that decides what an AI reads before it answers, built and measured properly.
Agents that know when to stop
Multi-step automations with a budget and a defined stop, instead of looping until the bill arrives.
Evaluation harness
A test that runs against your real cases and tells you when quality slips.
Cost, speed and drift monitoring
What it costs, how fast it answers, and whether it is quietly getting worse.
What you receive.
All of it is handed over, and yours to keep, whether or not we work together again.
- 01A documented architecture
- 02Retrieval quality that is measured, not assumed
- 03Infrastructure sized to your real load
- 04Cost and speed numbers you can defend to leadership
Read the full description of this service
Advanced AI Architecture is the infrastructure layer underneath everything else: the retrieval systems, agent orchestration, data pipelines, and serving infrastructure that decide whether an AI system is fast, reliable, and affordable to run — or none of those things. Most of what makes an AI product feel broken in production isn't the model itself. It's retrieval returning the wrong context, an agent looping without a stop condition, or a pipeline that can't handle the data volume it was actually going to see once real users showed up.
We take this on as a standalone engagement when a company already has models or vendor tools chosen and needs the architecture around them to actually hold — a retrieval system that returns the right documents instead of merely plausible ones, an agent framework that fails safely instead of spinning, or a data pipeline that doesn't fall over the first time real volume hits it.
In practice this looks like: a support tool that returns the wrong document because chunking split a policy across two fragments and neither one alone answers the question; an agent that calls the same tool nine times because nothing told it when to stop; a nightly ingestion job that silently drops half a day's records the first time a source system changes its schema without warning. The architecture work is finding failure modes like these before a user does, and building the layer that catches them after.
What you get
A documented architecture, a retrieval or orchestration system with its own evaluation harness attached, infrastructure sized to your real production load rather than a guess at it, and cost and latency numbers you can defend to your own leadership because we measured them, not estimated them.
When this isn’t the right fit
If nobody has decided what the system should actually do yet, this is the wrong starting point — architecture serves a decision, and building it ahead of that decision usually means rebuilding it once the decision arrives. A single well-chosen API call solves plenty of problems on its own; we'll tell you when that's actually the case instead of architecting past it.
Case 01 / 01 · Manufacturing & Logistics
01 · Who they are
A software company that helps businesses manage deliveries and supply chains.
Questions people ask before buying
01 / 06
Does it cost anything to ask?
No. You describe one problem, plainly, in a few sentences, and we tell you if it is worth building. That answer is free. No discovery invoice, no proposal deck.
02 / 06
What if AI is the wrong tool?
We tell you. A simpler rule, or no change at all, is sometimes the honest recommendation, and we would rather say so than build something that should not exist.
03 / 06
How will we know it works?
Where an engagement carries a measurable target, that target is written into the contract with a defined test, not implied by a case study.
04 / 06
Who can see our data?
Client data is processed only for the engagement it was provided for, and only by the people working on it. Access is granted per project and removed when the engagement ends.
05 / 06
Do we own what you build?
Documentation and runbooks are written for the engineers who take it over. We would rather leave cleanly than become a dependency you cannot remove.
06 / 06
Can you guarantee the result?
No, and you should treat any firm that does with suspicion. The figures on this site are real, but each one came from one specific client. What we can do is write your target down and test against it.
Your move
Send the requirement. We will tell you what the architecture actually needs.
Next service · 03 / 06
Assistants your customers or your team can talk to. They remember the conversation, answer from your real information, and hand over to a person when they should.