Working AI belongs inside your product — not bolted on the side as a chatbot. Dhanush Technologies builds LLM-powered features, predictive models, and intelligent automation for startups and SMEs across India and abroad. Think smart search that understands intent, copilots trained on your domain, forecasting that actually forecasts. Since 2018 we've shipped 150+ projects, and our AI work follows the same rule: if it doesn't move a business metric, it doesn't ship.
Most AI projects die in the demo stage. Ours don't, because we start with your data and your unit economics, not the model. We prototype in days, evaluate against real benchmarks, and only then wire the winner into production — with guardrails, fallbacks, and cost controls baked in. You get engineers who have shipped RAG pipelines and fraud models to real users, not slide decks about "AI transformation".
Everything we ship becomes yours outright: versioned models, documented pipelines, an evaluation suite, and dashboards that show accuracy and spend per feature. Your team gets a handover that actually transfers knowledge — architecture docs, runbooks, and a working session with our engineers. When the next model generation lands, you're positioned to swap it in, not start over.
Deliverables that ship.
LLM Feature Development
Copilots, summarizers, and natural-language interfaces built on Claude or GPT, wired into your product with streaming, guardrails, and cost caps.
RAG Pipelines & Semantic Search
Retrieval-augmented generation over your documents and databases: chunking strategy, embeddings, vector store, and reranking tuned until answers cite the right source.
Predictive Models
Churn, demand, fraud, and pricing models trained on your historical data, validated against holdout sets, and served behind a clean API.
Intelligent Agent Workflows
Agents that read invoices, triage tickets, and draft responses — with human-in-the-loop checkpoints so automation never runs unsupervised where it shouldn't.
Evaluation & Guardrails
Automated eval suites, prompt regression tests, hallucination checks, and PII filters, so every model change ships with proof it got better.
MLOps & Cost Monitoring
Deployment pipelines, model versioning, drift detection, and per-feature token spend dashboards on your cloud — you see what AI costs and earns.
Technologies we reach for
Asked before, answered straight
How do AI projects with you typically start?
With a paid two-to-three-week discovery sprint. We audit your data, define the metric the AI must move, and build a working prototype against real samples. You get a go/no-go recommendation with cost projections. If the numbers don't work, we tell you — about a third of ideas stop there, and that saves everyone money.
How long until an AI feature is live in production?
A focused LLM feature — RAG search, a summarizer, a support copilot — typically ships in 6–10 weeks including evaluation and hardening. Custom predictive models run 8–14 weeks depending on data quality. Data cleanup is usually the schedule risk, not the modeling, and we flag that in week one.
Who owns the models, prompts, and training pipelines?
You do, fully. Code, prompts, fine-tuned weights, evaluation data, and pipelines transfer to your repositories and cloud accounts on final payment. We build on your API keys from day one, so there is no migration or vendor lock-in with us. The only exceptions are third-party model licenses, which sit with the provider.
Models drift and providers change APIs — who keeps it working?
That's exactly what our monthly retainer covers: drift monitoring, prompt updates when providers change models, cost optimization, and eval re-runs. Most clients take it; it's priced per feature, not as a percentage of build cost. If you'd rather maintain in-house, we train your team during handover instead.
Let's build yours.
Tell us where you want to go — we reply within one business day.
Start a ProjectPrefer to talk? +91 76740 91177 · WhatsApp · info@dhanushtechnologies.com