AI Consulting · AI Products · Pune → 14 countries
We build the AI your enterprise actually runs.
Aurelia Innovatives is an AI consulting and products company. We take enterprises from strategy to production systems — and we productized what we learned into platforms like Vibalitics and Zorat.
340+
production deployments
5
platforms in the product line
14
countries in production
91%
straight-through agent work
Building on the platforms enterprises trust
AnthropicMicrosoft Azure
Databricks
Snowflake
Hugging Face
Meta Llama
Mistral
PyTorch
Python
Go
KubernetesOpenAIAnthropicMicrosoft AzureDatabricksSnowflakeHugging FaceMeta LlamaMistralPyTorchPythonGoKubernetesThe state of enterprise AI
Enterprise AI has a delivery problem.
0%
of enterprises report no measurable productivity impact from AI in three years
0%
saw AI ROI payback in under a year — the other 94% are still waiting
0%
say AI insights can't be trusted without formal governance behind them
The AI is already paid for. What's missing is the enterprise that runs on it — that gap is an execution problem, and it's what we do.
One firm, two engines
We consult like partners.
We build like a product company.
Consulting practice
Strategy to production, end to end.
Eight practices — from AI strategy and agentic systems to fine-tuning and embedded teams. Every engagement ends in a running system with an owner, a metric, and a measured return.
Explore services →Product line
Consulting insight, productized.
Vibalitics for data intelligence. Zorat for governed agents. AI Business OS to run it all as one system — plus AIDLC and ontology tooling powering every engagement.
Explore products →Platforms & stack
Your stack, not ours.
We're stack-agnostic by design — every platform below runs in at least one of our production deployments today.
OpenAI
in productionAnthropic
in productionDatabricks
in productionSnowflake
in productionHugging Face
Microsoft Azure
Open-weights models
in productionPyTorch
in productionPython
in productionGo
in productionKubernetes
Already running something else?
We build on what your teams already operate — tell us your stack →
Services
Eight practices.
One standard: production.
01 / 08
AI Strategy & Readiness
For enterprises that need direction, prioritisation, and an execution-ready roadmap before committing more budget.
Value map & use-case portfolio
Prioritised 12-month roadmap
Data & platform readiness assessment
Governance and operating-model blueprint
How engagements run
From strategy to
systems that scale.
The model kitchen
We cook models,
not demos.
Deep learning architectures built from scratch and foundation models tuned on your data. Every run is gated by eval suites and shadow deployments before it touches production traffic — that's the AIDLC discipline.
4×
accuracy over prompting
60%
inference cost cut
2wk
first checkpoint
Epoch
01/12
Train loss
2.4340 ↓
Eval accuracy
52.0% ↑
[data] curated 1.2M training pairs · dedup 4.1% · pii-scrub pass
GPU cluster
8× H100 · util 90%
The techniques on the stove
LoRA / QLoRA
AdaptersLow-rank adapters tune 1–3% of a model's weights — domain fluency at a fraction of full fine-tuning cost, with adapters you can hot-swap per task.
⤷ Domain language, formats, and style at low cost
RLHF
AlignmentReward models trained on your experts' preferences, then reinforcement learning against that reward — the standard for teaching judgment, not just knowledge.
⤷ Judgment-heavy outputs reviewed by experts
DPO / PPO
Preference optimizationDPO tunes directly on preference pairs without a separate reward model; PPO optimizes the policy against one. We benchmark both and pick per task.
⤷ Aligning outputs to expert choices, stably
Quantization & distillation
EfficiencyW8A8/W4 quantization and teacher→student distillation — tuned small models that replace frontier-model calls at 60–90% lower inference cost.
⤷ Latency budgets and unit-economics targets
Eval harnesses
AIDLC gateGolden sets, judge pipelines, and regression suites run on every checkpoint. No model advances a gate on vibes — only on measured wins.
⤷ Every run — evals are the requirement spec
Drift & continual ops
Post-deployShadow deployments, A/B gates, drift detection, and trigger-based retraining — the model stays good after the launch party.
⤷ Keeping production accuracy from decaying
In your building
Engineers where
the work is.
The delivery model that made enterprise software land inside the world's hardest institutions — now the way we ship AI.
07 · Field practice
NewForward Deployed Engineering
Outcomes shipped inside your walls.
FDE pods embed with your operating teams — on-site or hybrid
Vibalitics & Zorat live against your data in week one
Weekly increments demoed on your real cases, not slides
Measured on your outcome, not our deliverables
08 · Fractional leadership
AI Architects as a Service
Principal-level judgment, fractional cadence.
A named principal architect, 2–3 days a week inside your org
Owns the reference architecture and chairs design reviews
Build-vs-buy calls with a written decision log
Keeps every vendor honest — including us
The ontology layer
AI that knows what
your business means.
Models operating on raw tables make expensive mistakes. Our ontology layer profiles your data estate into a living business graph — customers, accounts, products, obligations, and how they relate — so every model and agent reasons over meaning, not columns.
Entity resolution that surfaces fraud rings as connected structures
Customer 360 computed on the graph, not stitched across systems
Zorat agents grounded in context — every decision cites its path
Living lineage: the map stays current as schemas evolve
Products
Platforms born
from the field.
How we deliver
AIDLC discipline on
every engagement.
01
Structured phases
Defined phases instead of ad-hoc experimentation — clearer decisions early, less drift later, a reliable path from strategy to execution.
02
Tangible deliverables
Roadmaps, scorecards, blueprints, running systems. The goal is not more discussion but work that moves the number.
03
Quality gates
Eval suites, sign-off criteria, and shadow deployments — AIDLC discipline on every engagement, so surprises surface early.
04
Measured outcomes
Every initiative carries an owner, a metric, and a kill criterion. ROI is tracked from day one, not reconstructed later.
0+
Production AI deployments
0
Platforms in the product line
0
Countries in production
0%
Straight-through agent processing
Industries
Domain depth,
not domain tourism.
Featured work
Proof, not promises.
What clients say
"AI in financial services demands governance, trust, and execution working together. Aurelia brought all three — systems that are scalable, explainable, and aligned with the realities of a regulated business."
Head of Data & Analytics
Leading Indian payments processor
"What stood out was the discipline. Eval gates, shadow deployments, drift monitoring — they ship AI the way great teams ship software. Our clinicians trust the system because they can see how it thinks."
VP of Engineering
US healthcare technology company
"They embedded with our teams, not above them. Twelve weeks in, our own engineers were shipping models with the same rigour — that knowledge transfer was worth as much as the system itself."
Chief Technology Officer
Omnichannel retail chain
Your AI is waiting on execution.
A 30-minute conversation about your decisions and your data. If we don't think AI will move your number, we'll say so.