Custom AI Model Development: Built for Your Data, Your Domain

Service Overview

Off-the-shelf AI models are powerful, but they are built for everyone, which means they are optimised for no one in particular. When your business needs AI that understands your products, your documents, your customers' language, or your domain's terminology, a generic model will always underperform. Our custom AI model development practice closes that gap. We fine-tune large language models on your data so they speak your industry's language, build retrieval-augmented generation systems that ground answers in your own knowledge base, develop custom AI agents that execute real tasks in your systems, and engineer search and recommendation engines that surface the right result the first time. Every model we ship is trained, evaluated, and deployed against your specific success metrics, not a generic benchmark. No black-box APIs you cannot inspect, no models that hallucinate your product specs, no prototypes that work in a notebook and fall over in production. Just custom AI that performs measurably better than the generic alternative on your data, your tasks, and your terms.

How We Work — Our Process

A structured, transparent engagement model that ensures delivery quality at every step.

1

Use Case & Data Discovery

We define the exact task the model must perform, the success metrics that will judge it, and the data available to train or ground it. If data is missing or messy, we scope the data preparation work before touching a model.

Week 1

2

Approach Selection

Based on the task and data, we choose the right approach: fine-tuning a foundation model, building a RAG system, training a custom model from scratch, or combining techniques. You get a clear recommendation with trade-offs, costs, and expected performance.

Week 2

3

Data Preparation & Pipeline

We clean, label, and structure your data for training or retrieval. For fine-tuning we build the instruction dataset. For RAG we build the ingestion and chunking pipeline. This step determines whether the model succeeds or fails.

Weeks 3-4

4

Model Training & Tuning

We train or fine-tune the model, run hyperparameter experiments, and evaluate against your success metrics on a held-out test set. You see the numbers before we move to deployment.

Weeks 5-6

5

Evaluation & Benchmarking

We benchmark the custom model against the generic baseline on your real tasks, measure accuracy, latency, cost, and failure modes, and document the results in a format your team can audit.

Week 7

6

Deployment & Monitoring

We deploy the model behind an API with logging, monitoring, and fallback to the baseline if performance degrades. You get dashboards showing usage, accuracy, and cost in production.

Week 8

Why Choose Us

Our key differentiators that set us apart in the AI services landscape.

🎯

Your Metrics, Not Generic Benchmarks

We evaluate every model against your specific success criteria on your data, not a public leaderboard. If the custom model does not beat the baseline on your tasks, we tell you before you spend on deployment.

🛡

Data Stays Yours

We can fine-tune and train entirely within your cloud environment or on your infrastructure. Your training data, model weights, and proprietary knowledge never leave your control.

📊

Baseline Before You Build

We establish a generic-model baseline first so you know exactly what the custom model buys you in accuracy, latency, and cost before you commit to the build.

👥

Your Team Can Operate It

We hand over training pipelines, evaluation scripts, and deployment runbooks so your team can retrain, evaluate, and update the model without us. No permanent dependency.

🕔

Production-Grade, Not Notebook Demos

Every model ships with an API, monitoring, logging, and fallback. It is built to run in production under real load, not to impress in a presentation.

✦

Cost-Aware Model Choices

We do not default to the largest, most expensive model. We right-size the model to the task, so you are not paying GPT-4 prices for a job a fine-tuned 7B model handles better and cheaper.

What We Offer

Detailed breakdown of each offering within this service category.

1

Fine-Tuning LLMs

Fine-tune open-source or commercial foundation models on your domain data so they understand your terminology, follow your style, and perform your specific tasks better than any generic model. We handle dataset creation, training, evaluation, and deployment.

Instruction dataset preparation
Fine-tuned model with evaluation report
Deployment API and monitoring
Retraining pipeline and runbook

2

RAG System Setup

Build a retrieval-augmented generation system that grounds model answers in your documents, knowledge base, or database. Answers cite their sources, hallucinations drop sharply, and the system stays current as your knowledge updates.

Document ingestion and chunking pipeline
Vector database setup and indexing
Retrieval and generation pipeline
Source citation and confidence scoring

3

Custom AI Agent Development

Develop AI agents that do not just answer questions but take actions: query your systems, update records, trigger workflows, and complete multi-step tasks with tool use, memory, and guardrails.

Agent architecture and tool definitions
Tool integration with your systems
Memory and context management
Safety guardrails and human approval

4

AI Search & Recommendation Engines

Build custom search and recommendation systems that understand intent and context, not just keywords. Customers find the right product, document, or answer faster, with results ranked by relevance to your domain.

Search index with semantic retrieval
Recommendation logic and ranking
Integration with your frontend or app
Relevance tuning and evaluation suite

5

Custom Model Training

When fine-tuning is not enough, we train custom models from scratch or from open-source foundations for specialised tasks: classification, extraction, prediction, or generation unique to your business.

Training data pipeline and labelling
Model architecture and training
Evaluation against success metrics
Deployment with monitoring and retraining

Technology Stack

The tools, platforms, and frameworks we use to deliver this service.

OpenAI API	GPT-4o fine-tuning and base for reasoning tasks	Advanced
Anthropic Claude	Claude 3.5 Sonnet for long-context and analysis	Advanced
Llama / Mistral	Open-source foundations for fine-tuning and self-hosting	Advanced
Hugging Face	Model hub, training tools, and inference endpoints	Advanced
LangChain / LlamaIndex	RAG orchestration and agent frameworks	Advanced
Pinecone / Weaviate / Qdrant	Vector databases for retrieval systems	Advanced
PyTorch / Transformers	Custom model training and fine-tuning	Advanced
vLLM / TGI	High-throughput inference serving for open models	Intermediate
MLflow / Weights & Biases	Experiment tracking and model registry	Intermediate
Ray / Kubernetes	Scalable training and inference infrastructure	Intermediate

Use Cases & Industry Applications

Real-world scenarios where this service delivers measurable business impact.

Legal Services

Challenge: A legal tech startup found generic models could not reliably extract clauses, obligations, and risks from Indian contract templates, producing outputs that needed constant lawyer review.

Solution: We fine-tuned an open-source LLM on a labelled dataset of Indian contracts, teaching it to extract clauses, flag risks, and summarise obligations in the format the firm's lawyers expected.

Outcome: Clause extraction accuracy rose from 68% to 91%, lawyer review time per contract dropped by 55%, and the system handled contract types the generic model misclassified entirely.

Healthcare

Challenge: A healthtech company needed an AI agent that could answer patient questions grounded in their medical knowledge base, not the open internet, to avoid unsafe hallucinations.

Solution: We built a RAG system that retrieves from their curated medical content, grounds every answer in cited sources, and refuses to answer when confidence is low rather than inventing a response.

Outcome: Hallucination rate on medical queries fell to under 2%, every answer cited its source for clinician review, and the system handled 70% of patient queries without human escalation.

E-commerce

Challenge: A large catalogue retailer's search returned keyword matches that missed intent, so customers searching colloquial or regional terms found nothing even when the product existed.

Solution: We built a semantic search engine using embeddings fine-tuned on the retailer's catalogue and search logs, ranking results by intent match rather than keyword overlap.

Outcome: Search no-result rate dropped 60%, conversion from search rose 28%, and the system understood regional and colloquial product names the old search could not.

Financial Services

Challenge: An insurance company wanted an AI agent that could verify claims by querying their policy database, checking documents, and flagging suspicious claims for review, not just answer questions.

Solution: We developed a custom AI agent with tool access to their claims and policy systems, document verification capability, and rule-based fraud checks with human approval for payouts.

Outcome: First-pass claim verification automated for 65% of claims, average processing time fell from 3 days to 6 hours, and fraud flagging caught 18% more suspicious claims.

Engagement Timeline & Impact Metrics

Project Timeline

Phase	Duration	Key Deliverable
Discovery & Approach	Weeks 1-2	Approach recommendation and plan
Data Preparation	Weeks 3-4	Training or ingestion pipeline
Training & Tuning	Weeks 5-6	Model with evaluation metrics
Evaluation	Week 7	Benchmark against baseline
Deployment	Week 8	Production API with monitoring

Business Impact

Metric	Generic Model	Custom Model
Task accuracy	60-75%	85-95%
Hallucination rate	8-15%	Under 3%
Domain relevance	Generic	Domain-tuned
Inference cost	High	Right-sized
Data control	External API	Yours

Our Capabilities

Capability	Status
Fine-tuning on your data	Included
RAG system setup	Included
Custom agent development	Included
Evaluation and benchmarking	Included
Deployment and monitoring	Included
Ongoing retraining	Add-on

Pricing & Packages

Transparent pricing for every engagement size. All packages include post-delivery support.

Tier	Price	Timeline	Includes
Starter	₹79,000	4 weeks	1 fine-tuned model or RAG system with deployment
Growth	₹1,99,000	6 weeks	Custom model + RAG + agent with evaluation and API
Enterprise	₹4,99,000	8-10 weeks	Full programme + multiple models + retraining pipeline
Custom	On request	Flexible	Large-scale or multi-model enterprise deployment

What Is Included

Use case and data discovery
Approach selection with trade-offs
Data preparation pipeline
Model training or fine-tuning
Evaluation against success metrics
Baseline comparison report
Deployment API with monitoring
Runbook and retraining pipeline

If the custom model does not outperform the generic baseline on your success metrics, we do not charge for deployment and we hand over the evaluation showing why.

Book a Free Consultation

Speak with our AI experts about your specific requirements. We will assess your needs, recommend the right approach, and provide a detailed proposal within 48 hours.

Book Your Free Consultation →

Frequently Asked Questions

When does fine-tuning make sense versus RAG?

Fine-tune when you need the model to adopt a specific style, terminology, or behaviour across many interactions. Use RAG when you need answers grounded in your documents that stay current as knowledge changes. Many systems combine both, and we recommend the right mix during discovery.

Do we need a large dataset to fine-tune?

Not always. For instruction fine-tuning, 500 to 5,000 high-quality examples often beat 50,000 mediocre ones. Quality matters more than quantity, and we help you build a dataset that is sufficient without over-collecting.

Can we keep our training data private?

Yes. We can fine-tune open-source models entirely within your cloud or on-premise environment, so your data and the resulting model weights never leave your control. For commercial APIs we review their data usage terms with you before proceeding.

How do you prevent hallucinations?

For RAG systems we ground answers in retrieved sources with citations and refuse to answer when confidence is low. For fine-tuned models we evaluate on hallucination-specific metrics and add guardrails. No system is perfect, but we measure and report the rate honestly.

What happens when our data changes?

RAG systems update automatically as you add documents to the knowledge base. Fine-tuned models need periodic retraining, which is why we hand over the retraining pipeline. We also offer optional maintenance retainers if you prefer us to handle it.

How much does inference cost in production?

It depends on the model and traffic. Fine-tuned open-source models self-hosted can be cheaper per token than commercial APIs at scale. We right-size the model and provide cost dashboards so you see spend in real time and can set budgets.

Can your agents actually take actions in our systems?

Yes. Our agents use tool calling to query databases, update records, trigger workflows, and call APIs. We build guardrails, human approval for sensitive actions, and audit logs so you control what the agent can and cannot do.

What if the custom model does not beat the generic one?

We establish the baseline first and benchmark honestly. If the custom model does not outperform it on your metrics, we tell you before deployment, do not charge for it, and hand over the evaluation so you understand why.

Related Services

Explore other AI services that complement this offering.

Explore All Services

Browse our complete range of AI business services and AI model services.

View All Services →