What kind of AI solutions does Rakri AI build?

We build four types of AI solutions: Custom AI Systems (end-to-end), Self-Hosted Private Models (fine-tuned LLMs on your infrastructure), AI SaaS Applications (full product development), and AI Workflow Automation (automating repetitive business processes).

Do I own the code and models Rakri AI builds?

Yes, 100%. Everything we build — code, models, data pipelines — is fully owned by you. No vendor lock-in, no recurring license fees for what we build.

How long does a typical AI project take?

Most projects are delivered within 3-6 weeks, depending on complexity. We scope honestly upfront so there are no surprises.

Is my data kept private?

Absolutely. We specialize in self-hosted and private AI deployments. Your data never leaves your infrastructure unless you choose otherwise.

Case Studies / Primastat

Natural-Language BI Platform — Turning Plain English Into Live Business Intelligence

Client

Primastat, India

Context

Primastat runs VizKraft, a production BI platform. Non-technical users needed to query business data without writing SQL, across four different underlying database engines.

The problem

General-purpose LLMs asked to write SQL against real schemas hallucinate columns, joins, and syntax that doesn't match the actual database — unacceptable in a product where the query runs directly against live business data.

Approach

We fine-tuned a large language model specifically on this task rather than relying on a general-purpose model with prompting alone.

Fine-tuned Kimi K2.5 on 5,000+ real query pairs across 12 database schemas, deliberately including multi-table joins, CTEs, and window functions.
Built a schema-injection pipeline (FastAPI, LangChain, SQLAlchemy) that resolves a client's live database schema at query time and injects it directly into the model's context.
Every generated query is syntax-highlighted, editable, and re-executable — technical users can audit and correct exactly what the AI decided to run.
Streamed results through Kafka across a three-service architecture (React SPA, Node.js orchestration, Python FastAPI) so charts render incrementally as query results arrive, in real time.

Fine-tuning on real schema/query pairs — not just prompting a general model — is what took hallucination from a recurring product risk to a rare, correctable edge case.

Architecture

Plain-English question→Live schema injection→Fine-tuned Kimi K2.5→Generated SQL

Generated SQL→FastAPI execution layer→Kafka event stream→Interactive chart

Outcome

70%+

Reduction in hallucinated queries vs. base model

<1.5s

Plain-English question to interactive chart

30+

Paying clients served in production

The platform is live in production with zero major AI-related incidents to date, and every generated query remains fully visible and editable by the end user.

Stack

Fine-tuned LLM (Kimi K2.5)FastAPILangChainSQLAlchemyReactNode.jsApache KafkaDockerGitHub Actions CI/CDPostgreSQLMySQLMongoDBSQLite

Metrics reported by client and Rakri AI engineering logs as of the current engagement period.

Have a similar nl-to-sql problem?

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Approach

We fine-tuned a large language model specifically on this task rather than relying on a general-purpose model with prompting alone.

Fine-tuned Kimi K2.5 on 5,000+ real query pairs across 12 database schemas, deliberately including multi-table joins, CTEs, and window functions.

Built a schema-injection pipeline (FastAPI, LangChain, SQLAlchemy) that resolves a client's live database schema at query time and injects it directly into the model's context.

Every generated query is syntax-highlighted, editable, and re-executable — technical users can audit and correct exactly what the AI decided to run.

Streamed results through Kafka across a three-service architecture (React SPA, Node.js orchestration, Python FastAPI) so charts render incrementally as query results arrive, in real time.

Fine-tuning on real schema/query pairs — not just prompting a general model — is what took hallucination from a recurring product risk to a rare, correctable edge case.