Generative AI · Custom LLM development
Off-the-shelf models don't know your contracts, your codes, or your customers
Banao develops custom LLMs trained on your domain data — fine-tuned or adapted from a curated base — so the model answers in your vocabulary, follows your output format, and handles the edge cases your business actually encounters.
We treat the training pipeline, the evaluation harness, and the deployment stack as one deliverable. The model you receive is measured against your real tasks, not a benchmark that has nothing to do with your use case.
Banao— We fine-tune and run generative models on our own demand-generation and hiring workflows before we build yours.
What a Banao custom LLM development engagement includes
A production-ready custom LLM is a trained model, an evaluation harness, and a serving stack — we build all three.
Use-case definition and feasibility
We pin the exact task the model must perform, identify where a base model falls short, and test feasibility before training starts — so budget is spent on a model that can succeed.
Data audit and preparation
We assess your proprietary data for volume, quality, and coverage, then clean, deduplicate, and structure it into a training-ready dataset aligned to your target task.
Base model selection and fine-tuning
We select the right base — open-weight or licensed — and fine-tune it on your data using LoRA, QLoRA, or full-parameter approaches depending on your compute constraints and accuracy targets.
RLHF and preference alignment
Where output quality depends on nuanced judgment, we run reinforcement learning from human feedback so the model learns your preferences, not just your patterns.
Domain evaluation and red-teaming
We build an evaluation suite from your real task distribution, score the fine-tuned model against it, and red-team it for failure modes before it touches production traffic.
Quantisation and serving infrastructure
We quantise and optimise the model for your target latency and cost, then deploy it — on your cloud, on-premise, or in a private endpoint — with monitoring for model drift.
Retrieval augmentation on top of the fine-tuned model
We layer RAG over the fine-tuned model where the task needs current or large-corpus knowledge the training data cannot hold, so parametric knowledge is grounded in live facts.
Ongoing evaluation and re-fine-tuning
As your domain evolves, we maintain the evaluation suite and schedule re-fine-tuning cycles, so the model does not degrade against new terminology or updated policies.
Fine-tuning versus prompting — and when each earns its cost
Most custom LLM work starts with a base model and adapts it, not builds from a blank weight matrix. The decision is about where the base model's failure mode sits: if it fails on vocabulary and format, fine-tuning on hundreds of well-labelled examples may close the gap in days. If it fails on reasoning structure or domain knowledge that isn't in any public corpus, deeper adaptation — or a different base — is the honest answer.
We make that call at the start of a Discovery Sprint, on your actual inputs, before you commit to a training budget. The Sprint produces the model selection rationale, the training data audit, and the evaluation plan that defines what success looks like — and whether fine-tuning alone will get you there.
Parameter-efficient fine-tuning (LoRA / QLoRA)
Adapts a large base model with a fraction of the compute by training only low-rank adapter matrices — the right approach when domain vocabulary and output format are the main gaps.
Full fine-tuning on curated domain data
Retrains all parameters on your proprietary corpus — justified when the task is structurally different from anything in the base model's pre-training and partial adaptation undershoots.
Continued pre-training on domain corpora
Extends the base model's pre-training on your internal documents before task fine-tuning — closes deep knowledge gaps that instruction-tuning alone cannot fix.
Evaluation is the work — not a check at the end
A custom LLM that improves benchmark scores but fails on your actual inputs is not a custom LLM — it is a fine-tuned model someone ran without checking whether the task matches the benchmark. The evaluation suite we build is drawn from your real distribution: the prompts you send today, the edge cases your team flags, and the adversarial inputs a user will try within a month of launch.
We run evaluation before and after every training change, score against your acceptance threshold, and give you a readable report that shows exactly where the model passes and where it does not. That report is how you decide to ship — not a vibe check on a handful of examples.
Task-level scoring against your real distribution
We sample from your actual input population, not a generic held-out set, so the evaluation score predicts real-world accuracy rather than benchmark performance.
Regression gates on every training change
A prompt-tuning change or a data refresh re-runs the full suite automatically. No change ships if it regresses a previously passing case.
Model cards and audit-ready documentation
We produce a model card covering training data provenance, known failure modes, and recommended use boundaries — the artefact your risk and compliance teams will ask for.
We run custom language models on our own business before we build yours
Vikaas, Banao's demand-generation system, runs on a language model fine-tuned on our own content and outreach data — not on a generic prompt to a base model. We use it to qualify leads and draft outreach across our own ~300-person operation. InterviewGod uses a fine-tuned model to assess applicants against role-specific criteria, not broad capability.
Building and operating fine-tuned models under our own business pressure is a different discipline from shipping one and handing it over. The evaluation standards and deployment constraints we apply to our own models are the ones we bring to yours.
- VikaasA fine-tuned generative model we run on Banao's own demand generation — not a generic base-model prompt.
- InterviewGodA fine-tuned model Banao uses to screen its own applicants against role-specific criteria every week.
Where we develop custom LLMs
India
Bangalore and Chandigarh hold the ML engineering bench — training infrastructure, data engineering, and evaluation at cost structures that make iterative fine-tuning feasible for mid-market budgets, under the DPDP Act.
UAE
From Dubai we develop for GCC enterprises that need model training and inference to stay within UAE boundaries under the PDPL, including on-premise deployment on client infrastructure.
US & UK
For US and UK clients we develop to SOC 2 and UK GDPR standards, with data-provenance documentation and model cards that satisfy compliance and legal review.
When custom LLM development is not the right call
A fine-tuned model is a meaningful engineering investment. We will tell you before you start whether it is the right one:
- Prompt engineering closes the gap: if careful prompting and a well-chosen base model already produce acceptable outputs on your task, fine-tuning is cost that won't earn back.
- You don't have enough proprietary data: fine-tuning on a thin, noisy dataset often makes the model worse, not better. We assess data sufficiency in the Discovery Sprint before any training starts.
- The task is too broad: a model fine-tuned to do everything in a domain ends up doing nothing well. Scope the task first; fine-tune against that scope.
- Latency or cost doesn't justify custom weights: if a RAG pipeline on a smaller base model meets your accuracy target at a fraction of the serving cost, that is the better system.
How we start — validate the training approach before committing the budget
We don't quote a fine-tuning engagement off a brief. We audit your data and test the base model's gap first.
- AI Discovery Sprint2 weeks · fixed price
We audit your training data, identify the base model's failure modes on your actual inputs, and hand back a training approach, an evaluation plan, and a budget estimate — yours to keep. If you proceed, the Sprint is credited against the build.
- Data preparation and training
We clean and structure your dataset, run the fine-tuning pipeline, and iterate against the evaluation suite — evaluation gates block every training change, not just the final one.
- Deployment and ongoing calibration
We deploy the model to your serving infrastructure, monitor for drift against your real traffic, and schedule re-fine-tuning cycles as your domain evolves.
Frequently asked questions
What is custom LLM development?
It is the process of adapting a pre-trained language model — through fine-tuning, continued pre-training, or RLHF — so it performs a specific task accurately on your domain's vocabulary, formats, and edge cases. It includes the training data pipeline, the fine-tuning run, the evaluation suite, and the serving stack.
When does fine-tuning a model make sense versus prompting a base model?
Fine-tuning earns its cost when the task requires consistent output format, domain-specific vocabulary the base model lacks, or accuracy on a narrow task that prompt engineering alone cannot reach. We test the base model's gap on your actual inputs in a Discovery Sprint before recommending it.
What data do we need to start a custom LLM development project?
Volume and quality matter more than raw size. Hundreds of high-quality labelled examples can outperform thousands of noisy ones. We audit your available data in the Discovery Sprint and tell you whether it is sufficient, what gaps exist, and whether synthetic augmentation is worth considering.
How do you evaluate whether the fine-tuned model is ready to deploy?
We build an evaluation suite from your real task distribution — actual prompts, edge cases, and adversarial inputs — and score the model against it before any change ships. The model must pass an agreed accuracy threshold on that suite before it touches production traffic.
Can you deploy the model on our own infrastructure rather than a cloud API?
Yes. We quantise and optimise the model for your target hardware, then deploy it on-premise or in a private cloud endpoint — including on air-gapped infrastructure where data cannot leave your environment. This is common for GCC clients with data-residency requirements.
How long does custom LLM development take?
A Discovery Sprint runs two weeks and produces the training approach and evaluation plan. A fine-tuning engagement then typically runs six to twelve weeks depending on data preparation complexity and the number of evaluation iterations required. Banao's ML bench in Bangalore and Chandigarh means work starts in days, not months.
Do we own the trained model and the training pipeline?
Yes. We hand over the fine-tuned weights, the training scripts, the evaluation harness, and the model card. You are not dependent on us to re-run training or extend the model.
What is the difference between fine-tuning and RAG, and do we need both?
Fine-tuning adapts the model's parametric knowledge and output behaviour — it learns your vocabulary and format. RAG grounds the model in live or large-corpus knowledge it cannot hold in weights. They solve different problems and often work together: a fine-tuned model with RAG layered on top is common for tasks that need both domain accuracy and current facts.
Bring the task your base model keeps getting wrong
Show us where a generic model fails on your domain inputs. In 45 minutes we will tell you whether fine-tuning closes the gap — and what a production build would take.
Book a 45-min scoping call