Bengaluru · AI engineering
We help Bengaluru product teams ship AI features that hold up in production, covering retrieval, evaluation, guardrails and cost control, working remotely alongside your engineers.
This page brings together
AI projects in Bangalore rarely start from zero. There is usually a product, an engineering team and a list of ideas: a support copilot, semantic search over internal docs, a Kannada or Hindi voice flow. What is missing is time for the unglamorous parts: evaluation sets, retrieval tuning, prompt versioning, monitoring and fallbacks.
That is where we fit. We build AI features as software your team can own, with tests and documentation. We say plainly which approach a problem needs (a hosted model call, retrieval-augmented generation, workflow automation or custom training) and where human review stays in the loop.
Local context
SaaS companies, capability centres, fintech and healthtech firms shape the AI work here.
SaaS teams in Koramangala, HSR Layout and along the Outer Ring Road want AI inside an existing product, not a bolted-on chatbot.
Capability centres in Whitefield, Manyata and Electronic City need internal copilots over Confluence, Jira or SharePoint that pass a parent company’s security review.
Fintech and insurance products need extraction from bank statements, KYC documents and claims, with human review for anything that affects a decision.
Users switch between Kannada, English, Hindi, Tamil and Telugu, so Indic speech and translation models often enter the design.
Buyers increasingly want data kept in Indian cloud regions and personal data handled in line with the DPDP Act.
Chunking, embeddings, hybrid search and re-ranking over your documents, with cited answers.
Test sets from real queries, automated and human scoring, and regression checks before each change.
One service for model calls, with provider routing, caching, rate limits and spend tracking.
Structured data from invoices, statements or forms, with a review queue for low-confidence fields.
Speech-to-text and translation in Indian languages via hosted APIs or open models such as IndicTrans2.
PII redaction, topic limits, prompt-injection checks and dashboards for quality, latency and cost.
Solutions
Support and sales chatbots for web, app and WhatsApp that answer from your own content and hand off to your team.
Search and question-answering over your documents, tickets and product data, with hybrid retrieval, citations and permission-aware results.
Data extraction from invoices, KYC documents, statements and forms into your systems, with validation rules and human review for uncertain fields.
Automations for repetitive back-office work, with AI steps that read, classify and draft, and approvals where decisions matter.
Multi-tenant SaaS products with AI at their core, built with tenant isolation, usage metering, model routing, evaluation and subscription billing.
Industries
Cost drivers
A labelled test set built with your domain experts is valuable work, and it needs their time too.
Hosted APIs are billed by usage to your account; self-hosting open-weight models adds GPU infrastructure and operations.
Scanned PDFs or permission-scoped wikis need parsing and access-control mapping before retrieval works well.
Enterprise reviews, in-region deployment and audit logging add documentation and engineering.
Estimates are written from your scope, with the effort and assumptions behind each line item.
How pricing worksProcess
We agree the task, users, success measures and which data the system may see.
You getOne-page AI brief
A time-boxed prototype on your real data, measured against a small evaluation set.
You getSpike report with measured results
Your repository or ours, with pull requests your engineers review.
You getTested service, dashboards and runbook
Your team takes ownership through documentation and pairing, or we stay on for monitoring and model updates.
You getHandover pack
Work
AI solutions
An illustrative AI assistant that answers order, return and product questions from a D2C brand's own policies and order data, with sources and a human hand-off.
Runs on
Built with
FAQ
Yes, and for teams with in-house engineers we usually prefer it. We work through your repositories, CI pipelines and review process, using access you grant and can revoke. Code follows your conventions, your engineers review our pull requests, and infrastructure is defined as code in your cloud account, which keeps handover simple.
We build an evaluation set from real or realistic queries, agree scoring criteria with your domain experts, and measure accuracy, groundedness, refusal behaviour, latency and cost for every change. Automated scoring catches regressions; human review checks what automation misses. Shipping is a decision your team makes against those numbers. No LLM system is error-free, so we also design fallbacks and escalation paths.
In most cases, yes. Several hosted models are offered through cloud platforms with Indian regions, though availability varies by model and provider. Open-weight models can run on GPU instances in the Mumbai or Hyderabad regions or on your own hardware. Vector stores, logs and application data can all stay in-region. We map everywhere personal data travels, which also supports your obligations under the DPDP Act.
Yes, with honest caveats. Large hosted models handle Hindi and other major Indian languages reasonably for text, but quality varies by language, domain and code-mixing. For speech and translation we compare hosted APIs with open Indic models on your real audio or text. Each language needs its own evaluation set, which adds cost, and we recommend human review of new-language output until results are stable.
Next step
Share the use case and a data sample. We will reply with an honest feasibility view and a line-item estimate.
