Solutions/LLM Fine-Tuning & Deployment Partner, Bengaluru

4-8 weekspilot to production·

95%+milestone adherence·

99.3%SLA stability

LLM Fine-Tuning & Deployment Partner, Bengaluru

LLM Fine-Tuning & Deployment

LLM fine-tuning (LoRA, QLoRA) on Llama, Mistral, Qwen, and others

Training data curation and quality pipeline

Systematic model evaluation and benchmarking

GPU-optimized inference deployment (vLLM, TGI, TensorRT-LLM)

Hybrid routing architecture (fine-tuned + API models)

API-based LLM integration (Claude, GPT-4, Gemini)

Start a project See our work

Trusted by 100+ innovative teams

Adobe

BCCI

Brigade Group

Cleartrip

Design Cafe

DRDO

Kotak Mahindra Bank

Mahindra

Metro Cash & Carry

NewsLaundry

Rapido

Reliance Jio

Urban Company

Abhibus

Engagedly

Adobe

BCCI

Brigade Group

Cleartrip

Design Cafe

DRDO

Kotak Mahindra Bank

Mahindra

Metro Cash & Carry

NewsLaundry

Rapido

Reliance Jio

Urban Company

Abhibus

Engagedly

What we build

From API-based prototypes to fine-tuned production models, we help product teams navigate the build-vs-buy decision for LLM-powered features.

End-to-end implementation covering training data curation, model fine-tuning, evaluation, GPU-optimized deployment, and hybrid routing architectures that balance cost and quality.

Built for teams like yours

Product teams evaluating fine-tuning vs API for their LLM features
Companies with high token volume looking to reduce LLM API costs
Enterprises with data privacy requirements needing self-hosted models
Engineering teams deploying their first fine-tuned model to production
Startups building domain-specific AI products on open-source models

How we deliver

From discovery to production in weeks

Discovery

Map your workflows, identify high-impact opportunities, and quantify ROI potential.

Pilot Build

Build a focused MVP for your highest-impact use case in 4-6 weeks.

Production Scale

Harden, monitor, and expand — leveraging existing infrastructure for each new capability.

4-8 weeks

pilot to production

95%+

milestone adherence

99.3%

SLA stability

Book Architecture Call Get Estimate

LLM Fine-Tuning & Deployment Partner, Bengaluru Implementation

Plan and launch llm fine-tuning & deployment partner, bengaluru without delivery surprises

Use the same rollout pattern we apply in production programs: architecture review, risk controls, and measurable milestones from pilot to scale.

Architecture and risk review in week 1

Approval gates for high-impact workflows

Audit-ready logs and rollback paths

4-8 weeks

pilot to production timeline

95%+

delivery milestone adherence

99.3%

observed SLA stability in ops programs

Book Architecture Call Get Estimate

FAQ

Questions & Answers

Can't find what you're looking for? Get in touch.

We instrument your current API-based system to collect real usage data, token volume, task types, latency requirements, quality scores. Then we run a cost-benefit analysis that accounts for fine-tuning investment, hosting costs, quality trade-offs, and engineering maintenance. The data usually makes the decision clear.

A typical engagement takes 6-10 weeks: 2 weeks for data curation and initial experiments, 2-3 weeks for iterative fine-tuning and evaluation, and 2-3 weeks for production deployment and monitoring. We can compress this to 4 weeks for well-defined tasks with existing training data.

We deploy on the infrastructure that matches your requirements. Options include AWS (SageMaker, ECS with GPU), Google Cloud (Vertex AI, GKE with GPU), Azure ML, and on-premises servers. For cost-sensitive deployments, we use reserved GPU instances and optimize batch sizes for maximum throughput per dollar.