Solutions
Six practices that only pay off as one system
We are not a menu of services. Every engagement below assumes the layers underneath it — a model is only as good as the contract feeding it, and a product is only as good as the deploy path shipping it.
The practices
What we are hired to build
Each practice carries its own failure modes, its own stack and its own way of starting. Open one to see the argument, not the brochure.
How the work fits together
Six practices, one dependency order
Most of what arrives labelled as an AI problem is a data problem wearing a better badge. An answer engine that cites correctly needs a corpus with permissions attached. An automation line that clears four cases in five needs a service that can be called twice without charging twice.
So the layers below are not a product ladder — they are the order in which the work becomes safe to do. Nothing ships reliably until the layer under it has contracts, tests and a deploy path. Most engagements start somewhere in the middle and work outward in both directions.
The useful question at the start is never which practice you want. It is which layer is currently the constraint — and that is usually answerable in a fortnight.
- ExperienceExperience Engineering
The surface people touch. Budgeted for speed, tested for access, measured in the field.
- ProductProduct Engineering
Domain models, API contracts and the services that hold the rules the business argues about.
- AutomationIntelligent Automation
Work that clears without a person, and a review desk for the work that should not.
- AIApplied AI & Agents
Agents, grounded retrieval and the evaluation harness that scores them on every merge.
- DataData & Platform Engineering
Contracts, pipelines and one semantic definition per entity, which everything above reads.
- CloudCloud, DevOps & SRE
Infrastructure as code, a bounded deploy path and the SLOs the whole stack is judged against.
Requests travel down the stack. Guarantees — latency, correctness, cost, recovery — travel back up, and each layer can only promise what the one beneath it already holds.
What we hold ourselves to
Targets, with the basis attached
Digiaeon is a young firm and will not pretend otherwise. Every figure below is a design target or a typical range for this class of system — the basis line under each one says which. None of them is a result claimed on behalf of a named client.
- Inference cost reduction from tiered routing and prompt caching
- 30–60%Inference cost reduction from tiered routing and prompt cachingDesign target derived from published provider pricing — small-model tiers and cached prefix tokens are priced far below frontier input tokens. Actual saving depends on traffic mix.
- Freshness on the tables that feed live decisions
- Minutes, not overnightFreshness on the tables that feed live decisionsDesign target for the streaming path we build; batch-only estates typically sit at 6–24 hours
- Lead time from merge to production, with the pipeline as the only route
- Under 1 hourLead time from merge to production, with the pipeline as the only routeDesign target. The elite band in the DORA State of DevOps research is under one hour; we architect the release path to sit inside it from the first release.
- Straight-through rate on high-volume, well-structured document flows
- 60–85%Straight-through rate on high-volume, well-structured document flowsTypical range for mature IDP programmes on a single document type after calibration — not a launch-week figure, and lower for contract-style documents
- First-pass reduction in AWS spend on an untuned estate
- 15–30%First-pass reduction in AWS spend on an untuned estateTypical range for estates that have never had a structured FinOps pass — rightsizing, storage tiering, Graviton and transfer cleanup. Measured against your own Cost and Usage Report before and after, never assumed in advance.
- Largest Contentful Paint at p75 on mid-range mobile
- ≤ 2.5sLargest Contentful Paint at p75 on mid-range mobileGoogle’s published ‘good’ threshold for Core Web Vitals, tracked as a field target in RUM and backed in CI by a throttled mid-range mobile lab assertion on the same routes.
Where to start
Tell us which layer is the constraint.
A 45-minute working session on the system you actually have. We will say what we would build, what we would leave alone, and roughly what it costs.