We build and ship
production data & AI systems.
One senior engineering team from raw data through intelligent agents: architecture, build, and handover. No consulting decks, no junior bench, no lock-in. Working software your team owns on day one.
- Lakehouse & ETL
- Streaming
- ML engineering
- RAG & LLM apps
- Multi-agent systems
The problem
Most data and AI projects do not fail on ambition.
They fail on three things, and we see the same three in almost every company that calls us.
- 01
The data layer was never built for this
Pipelines patched over three years, four half-migrated warehouses, and no single source of truth. Every AI initiative stalls at the same question: where does the clean data actually come from?
Reports land at 3pm. Decisions get made on yesterday’s numbers.
- 02
Prototypes that never reach production
A notebook that works on a laptop is not a system. Serving, monitoring, retraining, evaluation, and cost control are where AI projects quietly die — usually after the demo has already been shown to the board.
The demo impressed everyone. It has been "two weeks from launch" since March.
- 03
You cannot hire for it fast enough
Senior data and ML engineers take four to six months to source, interview, and onboard — if you win the offer. Your roadmap does not have six months, and your current team is already fighting fires.
Two open reqs, nine months, zero hires. The roadmap slipped twice.
Why iAretes
We have shipped this, at scale, in production.
Three things we do that most firms do not. All three matter.
We write code, not slide decks
Most consultancies hand you a strategy PDF and disappear. We ship production systems: data pipelines, AI agents, ML services. Every engagement ends with working software your team can run.
Every project includes a recorded handoff walkthrough and full documentation.
One team from data to AI
The same engineers who design your data architecture also build your AI layer. No "data team" handing off to an "ML team." One team owns the full stack from ingestion to intelligent automation.
The person you talk to in week one is the same person deploying in week eight.
You will never depend on us
We build systems your team can own on day one after handoff. Clean code, full documentation, and hands-on knowledge transfer. If you never need us again, we did our job.
We have never had a client blocked post-handoff. That is the standard we hold ourselves to.
What we build
Three practices. One team. No handoffs.
Whether the problem sits in the data layer, the model layer, or the automation layer, the same engineers own it end to end.
Pipelines that finish before your standup
Built to scale
We re-architect the layer everything else depends on. Not a migration plan — a running system your team ships against.
- Re-architected batch jobs: partition pruning, broadcast joins, and incremental processing instead of full daily reprocessing
- Medallion lakehouses on Apache Iceberg or Delta Lake with schema evolution and time travel
- Event-driven streaming with Kafka, Flink, and CDC when sub-second freshness actually matters
- Autoscaling compute, so you stop paying for a fixed cluster that idles 21 hours a day
36h → 10m
Pipeline runtime, rebuilt for an e-commerce client
Models that survive contact with production
Beyond the notebook
The gap between a working model and a working product is infrastructure. That gap is most of the job, and it is the part we specialise in.
- Feature stores, training pipelines, and model serving with drift monitoring built in from day one
- RAG systems grounded in your proprietary data using pgvector, Pinecone, or Weaviate
- Custom models where off-the-shelf fails — vision, forecasting, and domain-specific scoring
- Cost control by design: content-hash caching, rules layers, and model routing so LLM spend stays predictable
2 models
Crop advisory + vision diagnosis, in production for Farm2Com
Automation that reads, reasons, and acts
Work that runs itself
Agents are only useful when they are wired into real systems and know when to escalate. We build the wiring and the guardrails, not the demo.
- Multi-agent systems on LangGraph: a coordinator routes work to specialised sub-agents with real tool use
- Document intelligence across contracts, invoices, and scanned PDFs with OCR and structured extraction
- Human-in-the-loop routing, so low-confidence cases escalate to the right person with full context attached
- Connected to your APIs, databases, Slack, and CI/CD — not a sandbox that needs a separate login
60%+
Support tickets auto-resolved for a B2B SaaS team
Industries
Where we have already solved this.
The stack changes. The failure modes do not. These are the sectors we have shipped into, and what the work usually looks like.
Catch the discrepancy before it becomes a penalty
A customs filing is only as good as the documents behind it. One mistyped container number or a currency misread is a held consignment and a re-filing.
- Field-by-field scrutiny of draft checklists against source documents
- Local OCR across invoices, packing lists, bills of lading and air waybills
- Deterministic verdicts with provenance down to the page and cell
- Per-tenant isolation and versioned rule configs for auditability
How we work
You will know exactly what you are getting, and when.
The same process whether we are building a lakehouse or a multi-agent system. No surprises. No slide decks. Weekly working demos.
- 01
Discovery
We audit your current systems, map every bottleneck, and define exactly what needs to change. You walk away with a clear technical roadmap and honest cost estimate before we write a single line of code.
- 02
Architecture
We design the data models, system architecture, and infrastructure plan, then review it with your team. You sign off on every technical decision so there are zero surprises later.
- 03
Build & Ship
Agile sprints with production-grade code and CI/CD from day one. You get weekly demos with working software, not status slides, so you see real progress every week.
- 04
Operate & Transfer
We hand you a system your team can run without us. Full documentation, recorded walkthroughs, and hands-on knowledge transfer. You own it completely with no vendor dependency.
Results we’ve shipped
Production outcomes, not demos.
Real projects, in production. Starting with the product we built for Cooper Compass.
Measurable impact
Numbers from systems that are running right now.
Every figure below comes from delivered client work, not a projection or a benchmark. The case studies behind them are on the work page.
99%
Reduction in ETL processing time
E-commerce platform rebuild
36h → 10m
Daily pipeline runtime
Same engagement, same data volume
~40%
Compute cost reduction
Autoscaling replaced a 24/7 cluster
60%+
Support tickets auto-resolved
B2B SaaS multi-agent system
~12h
Engineering hours reclaimed per week
Triage work removed from the team
<30s
Average first-response time
Down from hours of manual triage
18
Customs fields audited per checklist
Thavasi Scrutiny, 10 at zero tolerance
4 modes
Air, sea, road and rail rule sets
Mode resolved before any field is compared
How to work with us
Three ways in. All of them end with you owning it.
Most engagements start with an audit or go straight to a build. Pick whichever matches how much you already know about the problem.
Architecture Audit
2 weeks · fixed price
Start here if you are not sure what is broken.
We go deep on your existing data or AI stack and come back with a written architecture review, a ranked bottleneck map, and a costed roadmap you can take to your board.
- Full review of pipelines, storage, and serving
- Ranked bottleneck and risk map
- Costed roadmap with effort estimates
- Credited against a build if you proceed
Build & Hand Over
6–12 weeks typical
The default. A production system, owned by you.
Fixed scope, weekly working demos, and a system your team runs without us at the end. Discovery and architecture sign-off happen before we write production code.
- Architecture signed off before build starts
- Weekly demos of working software, not status slides
- Full documentation and recorded walkthroughs
- Knowledge transfer until your team is independent
Embedded Engineers
Monthly · cancel anytime
For roadmaps that needed velocity last quarter.
Senior data and AI engineers plugged directly into your team, your standups, and your repo. No ramp-up theatre, no junior bait-and-switch.
- Senior engineers only, in your sprint cadence
- Working in your repo and your CI/CD
- Scale up or down month to month
- No lock-in, no minimum term
Our toolkit
We pick the right tool. Not the trendy one.
Deep expertise across AI agents, ML engineering, data infrastructure, and product development.
AI & Agents
ML Engineering
Data Processing
Lakehouse & Storage
Streaming
AWS Cloud
Databases
Full-Stack
DevOps & Infra
Languages
Questions
The things people ask before they email us.
If yours is not here, ask it directly — we answer honestly, including when the answer is that we are not the right fit.
Usually within one to two weeks. We run a free discovery call first to confirm the problem is one we can genuinely help with — if it is not, we will tell you and point you somewhere better.
An architecture audit is fixed-price and quoted up front. Builds are quoted after discovery, once we both understand the scope. You get a number before we write a line of production code, and we do not start work on a moving estimate.
You do, entirely, from day one. Your repository, your cloud account, your infrastructure. We do not retain licences, hold keys, or build anything that depends on us being around.
Most of our work sits alongside an existing team rather than replacing one. Typically we take the piece nobody has the bandwidth or specific experience for — a lakehouse migration, a model serving layer, an agent system — and hand it back documented.
AWS most often, but we work across GCP and Azure, and on-premise where regulation requires it. We build on what you already run. We will tell you when a tool is wrong for the job, and we will not push a migration you do not need.
By not sending everything to a model. We cache by content hash so the same input is never processed twice, put a deterministic rules layer in front for the common cases, and route to smaller models where they are sufficient. On the Cooper Compass build this is what made per-shipment AI economically viable.
You get full documentation, recorded walkthrough sessions, and hands-on knowledge transfer until your team is running it comfortably. We stay reachable, but the goal is that you never need to call. We have not had a client blocked post-handoff.
Yes. We sign NDAs as standard, work inside your cloud boundary and access controls, and build per-tenant isolation where the product needs it. Thavasi Scrutiny runs OCR locally rather than shipping customs documents to a third-party API, and enforces per-tenant isolation at the repository layer.
Get in touch
Every week you wait, the problem compounds.
Tell us what is slowing your team down. You will get a reply within 24 hours from an engineer, with an honest assessment and exactly how we would approach it — including when the answer is that we are not the right fit.
24 hours
Reply time, from an engineer
Free
Discovery call, no obligation
Day one
You own all code and infrastructure
Zero
Lock-in, licences, or retainers