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enquiry:+91-9121642626Custom LLM development: your own AI, on your own terms
Tvisha is a custom LLM development company helping enterprises adapt, ground, and privately host large language models - so your AI knows your domain and your data never leaves your control. Part of our full range of AI development services.
What does custom LLM development actually mean?
Custom LLM development is adapting large language models to your business rather than using them off-the-shelf. That spans three approaches, often combined: grounding a model in your data through retrieval (RAG) so it answers from your documents; fine-tuning a model on your examples so it learns your domain language, formats, and judgement; and private deployment - hosting open-source or commercial models on your own infrastructure so sensitive data never leaves your environment. The right mix depends on your accuracy, privacy, and cost requirements - and choosing it is the first thing we help with. It sits within our wider AI & machine learning services.
RAG · Fine-tuning · Private hosting - the right mix for your accuracy, privacy, and cost
Tell us what you're looking for and we'll get back to you.
Our team is a call away in both India and the USA.
LLM development services, from model selection to production
We're model-agnostic: open-source or commercial, cloud or on-premise - chosen for your requirements, not our preferences.
RAG Systems on Your Data
Retrieval-augmented generation connecting an LLM to your documents, databases, and wikis - accurate, citable answers from your truth. Our dedicated RAG development practice covers this in depth.
Fine-Tuning & Adaptation
Models trained on your examples - your domain vocabulary, output formats, and quality bar.
Private LLM Hosting
Open-source models (Llama, Mistral and peers) deployed on your cloud or on-premise - zero data leaves your walls.
Model Selection & Benchmarking
We test candidate models against your real tasks and pick on evidence - accuracy, latency, and cost per query.
Safety & Evaluation
Evaluation suites, red-teaming, and output guardrails so the model behaves in production, not just demos.
LLM-Powered Applications
The model wired into real products - assistants, copilots, and document intelligence - often through an MCP server connecting it to your own tools and data, or as the engine of an AI agent.
LLMOps & Cost Control
Monitoring, versioning, token-cost optimisation, and retraining pipelines - the unglamorous work that keeps it reliable.
Why enterprises choose custom LLM development
The three drivers we see again and again: privacy, accuracy, and unit economics.
Data Sovereignty
Regulated industries running LLMs fully on-premise - legal, defence, BFSI, healthcare.
Domain Accuracy
Models that speak your industry's language instead of confident generalities.
Document Intelligence
Contracts, claims, and reports understood, extracted, and summarised at scale.
Internal Knowledge
A private assistant that has read everything your company ever wrote.
Cost at Scale
High-volume workloads moved from per-token APIs to owned open-source models.
Product Differentiation
SaaS founders embedding a model competitors can't copy off a shelf.
Our LLM development process, from requirements to a model you own
Our LLM development services run in small, measured stages - no long proposal cycle up front, each stage checked against your own data before moving to the next.
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01
Requirements & Data Audit
Accuracy targets, privacy constraints, volumes - and whether your data is ready.
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02
Model Selection
Candidates benchmarked on your real tasks: quality, latency, cost.
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03
Adapt & Ground
RAG, fine-tuning, or both - built and measured against your evaluation set.
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04
Private Deployment
Your cloud or on-premise, with security review and load testing.
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05
Operate & Improve
Monitoring, drift detection, and scheduled retraining as your data grows.
The model stack we work with
Every layer here is swappable. The exact mix changes from one engagement to the next, depending on your use case, budget, and data-privacy requirement.
Is a custom LLM right for your business?
RAG, LLM fine-tuning, and private hosting all pay off fastest under a few specific conditions. If most of these describe you, it's worth a conversation. If a general-purpose API already meets your accuracy and privacy bar, it's probably still the cheaper answer - and we'll say so on the first call.
Data that can't leave the building
Regulatory or contractual terms mean a public API is a non-starter, whatever the accuracy.
Generic models miss your domain
Off-the-shelf answers are confident but wrong often enough that people stop trusting them.
Per-token costs are adding up
Volume has reached the point where an owned model is cheaper than metered API calls.
Real data to ground or tune on
You have the documents, examples or transcripts a model would actually learn from.
General-purpose API vs. custom LLM development
A public API is the right call for plenty of teams. It stops being enough once your data, accuracy bar, or privacy requirements get specific.
- Answers drift from your domain's actual terms, formats, and edge cases
- Your data leaves your environment on every single call
- Cost scales per token with no ceiling as usage grows
- Grounded or fine-tuned on your own data and domain language
- Deployed privately - nothing leaves your environment
- Predictable infrastructure cost instead of unbounded per-token billing
Why enterprises trust Tvisha with their LLM work
The same reasons that make this a fit for you also make us the team to build it with.
Model-agnostic by design
Llama, Mistral, Claude, GPT, Gemini - we run candidates against your own tasks and let the results pick the winner.
Security-first delivery
Private hosting, data isolation, and evaluation gates are a defined stage in every engagement, not bolted on after launch.
Enterprise delivery discipline
20+ years and 500+ projects of software delivery experience sit behind every custom LLM engagement we take on.
A dedicated in-house team
100+ developers across two offices work your engagement directly, the same people from kickoff through production.
An enterprise LLM solutions company you can verify
Numbers you can check, not just claim.
Custom LLM development FAQs
The ones that come up on nearly every first call about custom LLM development.
What does custom LLM development actually mean?
It is adapting large language models to your business rather than using them off-the-shelf. That spans three approaches, often combined: grounding a model in your data through retrieval (RAG) so it answers from your documents; fine-tuning a model on your examples so it learns your domain language, formats, and judgement; and private deployment, hosting models on your own infrastructure so sensitive data never leaves your environment.
Do you fine-tune models or use RAG?
Both, and often together. RAG grounds a model in your documents so answers are accurate and citable; fine-tuning teaches a model your domain vocabulary, output formats, and judgement. Which combination makes sense is usually clear within the first working session, once we see your data and your accuracy bar.
Can the model run entirely on our own infrastructure?
Yes. We deploy open-source models such as Llama and Mistral, and their peers, on your cloud or on-premise, so zero data leaves your environment. This is the standard setup for regulated industries such as legal, defence, BFSI, and healthcare.
Which models do you work with?
We are model-agnostic - open-source or commercial, cloud or on-premise - chosen for your requirements, not our preferences. That includes Llama, Mistral, Anthropic Claude, OpenAI GPT, Google Gemini and models available through Hugging Face.
How do you make sure the model is safe before production?
Through evaluation suites, red-teaming, and output guardrails, so the model behaves in production and not just in demos. This is a defined stage in every engagement, not an afterthought before launch.
More AI and LLM services
Ready to work with an LLM development company?
Tell us about your data and requirements and we'll scope the approach - no generic estimate.
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