AI Solutions
Generation that stays on-brand and on-fact.
Content, documents and images generated inside your workflow, grounded in your data and reviewed before it ships.
Sound familiar?
- “Producing listings, proposals or descriptions takes hours per item.”
- “The generic AI writer you tried produced text indistinguishable from competitors'.”
- “You want AI features in your product but can't control quality or spend.”
Capabilities
What it does.
On-brand output
Prompts and examples derived from your existing approved material.
Grounded in your data
Generation constrained by your facts, which is what stops confident invention.
Review workflow
Draft, review, approve — with an audit trail on anything customer-facing.
Batch generation
Thousands of items processed without manual triggering.
In-product features
Generation embedded in your application, not a separate tool.
Model-agnostic
Swap providers as they improve or prices change without rewriting the feature.
How we deploy it
The path from pilot to production.
Most AI projects die between a good demo and a working deployment. This is the sequence that avoids that.
Collect gold standards
Twenty examples of output you'd be happy to publish. These do more than any prompt engineering.
Ground it
Product data, specifications and facts supplied to the model rather than recalled by it.
Evaluate
Generated samples scored against your standards before anything ships.
Add review
Human approval on anything customer-facing or contractual.
Instrument cost
Spend per output tracked and capped per feature and per user.
Data & privacy
Where your data goes, plainly.
This is the first thing enterprise procurement asks about AI, and most vendor pages avoid answering it.
- Enterprise API tiers exclude your inputs and outputs from model training.
- Generated content is owned by you under the major providers' terms.
- Source material stays in your storage; only what's needed is sent per request.
- Open-weight models available for fully on-premise generation.
What it costs to run
Most generation costs a few rupees per output. Long documents with large context cost more. We cap spend per user and per feature so it stays a controlled line item.
Technology
What we build it with.
- Anthropic Claude
- OpenAI
- Google Gemini
- Llama
- LangChain
- Python
- Next.js
We're not tied to a model vendor. Systems are built so a provider can be swapped as capability improves or prices move.
You do — the major providers assign output rights to the customer. Generated images carry more nuance around trademark and likeness, and we flag anything in your use case that warrants legal review.
Ground it in your actual data rather than the model's memory, require citations where accuracy matters, and keep a review step on anything customer-facing.
Detection tools are unreliable in both directions and we wouldn't build a strategy around evading them. The better goal is output that's genuinely useful and accurate, reviewed by a person before publication.
Yes, and this is where most implementations fail. We build from your approved examples and evaluate against them, rather than describing your tone in a prompt and hoping.
Start here
Tell us the process, not the technology.
The best AI projects start from an expensive manual task, not from a decision to use AI. Describe the task and we'll tell you honestly whether this is the right tool.