Applied AISoftware engineeringChandigarh, India
We turn scattered workinto systems thatcompound.
Digiaeon designs, builds and operates production software — agentic AI, data platforms and custom products. We join at the messy part and stay until it runs without us.
- Agentic workflows
- Retrieval that cites
- Evaluation harnesses
- Event-driven data
- Zero-downtime migrations
- Design systems
- Cost-governed inference
- SLOs that hold
The gap
Most AI programmes stop in the same three places.
None of them are model problems. They are engineering problems that were left for later, and later arrived during a customer incident.
The demo was the whole project
A prototype answers the twenty questions its author thought of. Production sends the other ten thousand. Without a held-out evaluation set built from real traffic, nobody can say whether the last change helped — so the team ships on instinct and rolls back on complaints.
Nobody owns the quality bar
A model provider ships a new version, tone shifts, an output schema drifts, and a downstream parser starts dropping fields in silence. There is no regression gate, no owner, no page. The first signal arrives as a customer, an auditor, or an invoice.
The data was never the AI problem
Retrieval is only as good as what it retrieves. Without contracts at the producer, an upstream rename breaks a pipeline quietly; without lineage, nobody can say where a number came from. The model gets blamed for a warehouse that was never finished.
We start at whichever of the three is actually blocking you — not at the one that demos best.
What we build
Six practices. One engineering standard.
Most engagements draw on two or three of these at once. They share a codebase, a review bar and an on-call rota, which is why the seams between them do not leak.
How the work runs
First call to something in production.
Five phases, each with a dated output. If a phase produces nothing you could show someone, it was theatre and we cut it.
45–60 minutes
Technical qualification call
One call with the engineer who would lead the work, not a salesperson. We ask what breaks today, who gets paged, what has already been tried, and what the constraint really is — budget, regulation, an existing team, or a date. If the honest answer is that a product already on the market solves it, we say so on the call.
→A written point of view on the problem, a rough shape for the work, and a yes or a no on fit.
2–3 weeks
Discovery and architecture
We read the code and the data before drawing anything. Access to a read replica, the existing repositories, the incident history and two or three of the people who operate the system daily. The output is an architecture with the alternatives we rejected and why, a risk register, and the parts we consider genuinely uncertain.
→Architecture document, decision records, sequenced delivery plan, and a fixed price for the first build phase.
4–8 weeks
Thin slice to production
One real path — a single document type, one customer segment, one workflow — taken end to end into production behind a flag, with CI, infrastructure as code, telemetry and an eval suite in place from the first week. Real users, small blast radius. This is where the estimates for everything else get corrected by contact with reality.
→A deployed slice serving live traffic, a pipeline anyone on the team can ship through, and dashboards with named owners.
6–12 weeks
Harden and widen
Coverage expands along the plan while the operational work runs in parallel: load and failure testing, SLOs with error budgets, cost controls, access reviews, and the second and third failure modes that only appear under real volume. The client’s own engineers commit to the repository from this phase onward, with review from our side.
→Full scope in production, SLOs agreed and monitored, load and chaos test results, and a cost model per unit of work.
2–4 weeks, then optional support
Handover and steady state
Runbook walkthroughs, a live incident drill run by the client team while we watch, credential and ownership transfer, and an honest list of the shortcuts taken and what they will cost later. After that the team either owns it outright or keeps us on a defined support window — never a dependency created by withheld knowledge.
→Runbooks, drill results, an accurate technical-debt register, transferred infrastructure ownership, and an optional support agreement.
Solution blueprints
The actual shape of what we ship.
Reference architectures, with the tradeoffs stated. Not case studies — these are how we build, published so you can judge the engineering before you hire it.
Industries
The technology rarely changes. The constraints always do.
- Financial ServicesCredit, fraud and compliance systems that can explain themselves to a regulator years later
- Healthcare & Life SciencesClinical and operational systems where being wrong is expensive — built with the citation attached
- Retail & CommerceCatalogue, search and demand systems that hold their budgets on the biggest sale day of the year
- Manufacturing & Supply ChainPlant-floor signal turned into planning decisions — without ever touching the safety layer
- Education & EdTechScoring, tutoring and credentialing that a candidate can dispute — and you can defend
- Logistics & MobilityRouting, ETA and exception systems that hold up when the network is noisy and the margin is thin
How we think
Opinions we will not trade away.
These decide what we build and, more often, what we refuse to.
We instrument before we optimise
The first commit on a performance problem is a trace, not a rewrite. Without spans, structured logs and a p99 that someone is watching, every optimisation is a guess wearing a benchmark. Often the fix is one unindexed query or a retry storm — the kind of thing an afternoon of OpenTelemetry data surfaces and a week of intuition does not.
Reversible decisions fast, irreversible decisions slow
A component name, a library choice, a page layout — decide in minutes and move; the cost of undoing it is one pull request. A database engine, a tenancy model, a public API contract, a PII boundary: those get written up with the tradeoff stated, because unwinding them costs a quarter. Most teams get this exactly backwards and argue for three days about the thing they could have changed in ten minutes.
Boring infrastructure until it genuinely hurts
Postgres until the query plan says otherwise. A queue before a stream. One service before four. Every distributed component added early buys a capability the team does not need yet and charges rent forever in operational surface. When a workload truly outgrows the boring option — analytical scans hitting the write path, fan-out that a single node cannot absorb — we move that one workload, and say so in writing.
If it isn’t scored, it isn’t shipped
Anything non-deterministic gets an evaluation set before it gets a feature flag: golden cases, adversarial cases, and every real failure ever reported, versioned in the repository and run on each pull request. This is what makes a prompt change or a model upgrade an engineering decision rather than an argument about whose examples were more convincing.
What we commit to
- To a first production cut
- WeeksTo a first production cutDesign target for a scoped first slice, not a full programme
- Evaluation suite runs
- Every PREvaluation suite runsStanding practice on every non-deterministic system we build
- You own the repository
- Day oneYou own the repositoryIP assigns to the client from the first commit
- The people who scope it, build it
- No benchThe people who scope it, build itHow the firm is staffed
Insights
Notes from the part nobody demos.
Written for engineers and the people who have to fund them.
Next step
Bring us the problem you keep deferring.
A 45-minute working session. We'll tell you what we'd build, what we'd not build, and roughly what it costs. No deck.