LLM assistants and chatbots
Support and sales assistants grounded in your own documents with retrieval, so answers cite your content instead of inventing it.
AI that earns its place in the product. We start from the task you want automated, pick the smallest model that does it reliably, and ship it with evaluation, guardrails and a cost per request you can predict.

Support and sales assistants grounded in your own documents with retrieval, so answers cite your content instead of inventing it.
Tool-using agents that read a form, query a system and complete a task, with human approval where the stakes justify it.
Detection, classification and OCR pipelines for quality inspection, document processing and monitoring.
Demand, churn and anomaly models trained on your history and delivered as an API your existing software can call.
Claude, OpenAI or self-hosted open models added to the product you already run, without a rewrite.
A test set, accuracy benchmarks, caching and token budgets, so quality and spend are both measurable after launch.
A call or a meeting at the Belagavi office where we work out what you actually need — and tell you if it is smaller than you thought.
A written scope, a milestone plan with dates, and a fixed price. No hourly billing, no moving numbers mid-project.
Designs first, then development in weekly increments on a staging link you can open at any time.
Cross-device testing, performance and security checks, then deployment to your infrastructure with your accounts.
Source code, design files and documentation handed over, followed by a warranty period and optional support retainer.
Usually yes. Most integrations sit behind an API layer and touch a few screens, so we can add an assistant, a summariser or a classifier without rebuilding the application underneath.
No. We use enterprise API tiers where training on your data is contractually off by default, and for genuinely sensitive workloads we deploy open models on infrastructure you control.
We refuse to guess. Discovery includes building a labelled test set from your real data, and the proposal quotes a measured accuracy target on that set along with the fallback behaviour when the model is unsure.
Model usage is metered, so we estimate cost per request during the prototype, then cut it with caching, smaller models for easy cases and prompt compression before go-live. You see the projected monthly bill before committing.
Tell us what you need. You'll hear back from a real engineer, usually within one business day.
24/7 Support
Our team is always on call for you
Live Consultation
Talk to a strategist before you commit
Guaranteed Results
Backed by transparent monthly reporting