Service · Aarohii AI Solution Private Limited
Generative AI development
A generative AI development company designs software that uses large language models in a real product loop—then measures whether those outputs are still acceptable next month.
What we build
Production features: assistants grounded in your data (RAG), agents that call tools with approval, and application UX that shows uncertainty instead of hiding it. We integrate models you choose; we are not a model lab.
The common shape is narrow on purpose. A generative feature that can produce anything is hard to test, so it never gets tested, so it never gets trusted. A feature that produces one artefact — a draft reply grounded in the ticket history, a summary of one contract, a first-pass job description — can be evaluated, improved, and defended in a review. Background: what generative AI is.
Design decisions that matter more than the model
- What the feature is allowed to write, and which sources it may draw on.
- Where the human signs off, and whether their job is review or proofreading. If it is proofreading, the feature is not ready.
- What the interface shows when the model is unsure — a confident wrong answer with no provenance is worse than no feature.
- What happens on failure: degrade, retry, or show an honest error.
- Who owns the eval set after handover.
How we keep it honest
Every serious feature gets an evaluation hook, a human review path for high-impact actions, and a way to turn the feature off. See the production checklist and vendor evaluation notes.
Generative does not mean unsupervised. Production systems still need evaluation, access control, and a person accountable when the output causes harm — and that person should be named before launch rather than identified afterwards.
Proof you can open
Captverse is Aarohii's AI business OS (CRM, sales, inventory, payments, agents). PixellPeep is AI-assisted UI testing. Auvora is the VMS. Open them without an NDA — it is a more useful signal than a slide about methodology.
Related: LLM development · RAG vs fine-tuning.
What we will not do
We do not fine-tune by default, we do not train foundation models, and we do not ship a generative feature whose output nobody has agreed to be responsible for. If the request is a marketing chatbot bolted to a static site, we will say it will not earn its maintenance.
How an engagement runs
- 20-minute fit call — we say yes or no.
- Written plan — scope, success tests, and what we will not do.
- Build with evaluation hooks and weekly demos you can inspect.
- Support after launch — we stay for the messy month, not just demo day.
Open the proof without an NDA: PixellPeep, Captverse, Auvora, ViraQueue. Buying notes: what drives cost · how to choose a company.
Questions
Do you fine-tune models by default?
No. Most product problems are retrieval, UX, evaluation, and ops. Fine-tuning is a later choice. See RAG vs fine-tuning.
Which model vendors do you use?
We pick from current production vendors against latency, cost, data handling, and failover — not a single brand loyalty. See evaluating LLM vendors.
How do you stop a generative feature from drifting after launch?
An eval set that runs before every release, traces for every request, and a named owner who reads the results. Drift is detected by tests, not by complaints.
Can you work inside our existing product and stack?
Yes — that is the normal case. The feature has to live in the product your operators already use, or it becomes a second tool nobody opens.
If the problem maps to work we actually ship, we will say so in 20 minutes.
Request a fit call