AI Solutions
Models tuned to your task,measured properly.
Fine-tuning, evaluation and self-hosted deployment — plus an honest answer on whether you need it at all.
Sound familiar?
- “Prompting alone isn't getting the consistency your use case needs.”
- “Your data can't leave your infrastructure, ruling out hosted APIs.”
- “You have no way to tell whether a prompt change made things better or worse.”
Capabilities
What it does.
Evaluation suites
Scored test sets from your real cases, so quality is measurable across changes.
Fine-tuning
Task-specific tuning where prompting has genuinely plateaued.
Self-hosting
Open-weight models in your VPC or on-premise, sized and benchmarked.
Guardrails
Input and output filtering, jailbreak resistance and topic boundaries.
Model routing
Cheap models for routine steps, strong models for hard reasoning.
Observability
Traces, latency and cost per request, with regression alerts.
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.
Build the eval first
Without measurement, every subsequent change is a guess. This is the step people skip.
Exhaust prompting
Better prompts and retrieval solve most problems more cheaply than fine-tuning.
Fine-tune only if needed
When prompting plateaus and you have enough labelled examples to justify it.
Deploy with guardrails
Filtering, rate limits and cost ceilings before it faces users.
Monitor for drift
Provider model updates change behaviour. Evals catch it; impressions don't.
Data & privacy
Where your data goes, plainly.
This is the first thing enterprise procurement asks about AI, and most vendor pages avoid answering it.
- Self-hosted deployment keeps every token inside your infrastructure.
- Fine-tuning data never leaves your environment on open-weight models.
- Hosted enterprise tiers contractually exclude your data from training.
- Full request logging under your control for audit and incident review.
What it costs to run
Self-hosting a 7–14B model costs roughly ₹40,000–₹1,50,000 a month in GPU capacity. It only beats hosted APIs at sustained high volume — below that, hosted is cheaper and better, and we'll say so.
Technology
What we build it with.
- Llama
- Mistral
- vLLM
- PyTorch
- Hugging Face
- LangSmith
- Python
- AWS / GCP
We're not tied to a model vendor. Systems are built so a provider can be swapped as capability improves or prices move.
Industries
Where it applies.
Usually not. Better prompting and retrieval solve most problems at a fraction of the cost and complexity. Fine-tuning earns its place for consistent formatting, a specialised domain vocabulary, or shrinking a task onto a cheaper model.
Only if policy requires it or your volume is genuinely high. Below sustained heavy usage, hosted frontier models are cheaper, better and less operational work. We'd rather tell you that than sell you a GPU cluster.
A scored test set of your real cases. Without it you can't tell whether a prompt change, a model upgrade or a provider's silent update helped or hurt. It's the single highest-value thing to build first.
Input filtering, output validation, strict system boundaries and adversarial testing. No approach is perfect, so we also limit what the model can actually do — constrained capability beats perfect filtering.
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.