Generative AI Development Services That Turn Your Proprietary Data Into Competitive Advantage
Generic AI gives you generic answers. Custom Generative AI — built on your data, your workflows, and your domain — gives you a competitive advantage no one else can copy.
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Generative AI Development Services That Turn Your Proprietary Data Into Competitive Advantage
Not generic bots. AI agents trained on your products, policies, data, and brand voice.
From strategy session to production deployment with full system integration.
Scale support, sales, and operations 24/7 without hiring.
You've Tried AI. Here's Why It Didn't Stick.
Most businesses aren’t failing at Generative AI because the technology doesn’t work. They’re failing because they’re using a model built for someone else’s problem on their specific business challenge. If any of these hit home, you’re not alone — and you’re in the right place.
ChatGPT Gives You Confident Wrong Answers
You asked your AI about your own product. It made something up — politely and convincingly. Hallucination isn’t a bug you can patch. It’s what happens when a model doesn’t know your domain. The solution isn’t better prompts. It’s a model that knows your data.
Your Team Gets Different Answers Every Time
Two people ask the same question. They get two different outputs. One is wrong. Consistency and accuracy require grounding — RAG systems, fine-tuning, knowledge bases — not a chat interface bolted onto a generic LLM.
You Built a POC. It Never Made It to Production
The demo was impressive. Engineering signed off. Then it hit real data volumes, edge cases, and integration requirements. It fell apart. Building for demo is different from building for production — and most vendors stop at the demo.
You're Paying Per Token for a Model That Doesn't Understand Your Business
API costs compound fast when you’re compensating for a model’s ignorance with longer prompts, chain-of-thought workarounds, and retry logic. A fine-tuned or purpose-built model does more with less — smaller context windows, lower latency, lower cost per inference.
Legal and IT Won't Approve Sending Your Data to OpenAI
Regulated industries, sensitive IP, and privacy-conscious enterprise teams can’t send customer data to a third-party model. On-premise deployment and private cloud solutions exist — but most vendors don’t offer them.
Your Competitor Just Launched Something You've Been 'Evaluating' for 18 Months
Generative AI isn’t a future capability anymore. It’s a current competitive advantage. Every month without a production system is a month of compound disadvantage.
Custom Generative AI Solutions Built For Your Business — Not a Template
Every service below starts with your data, your workflows, and your specific use case. Nothing is pre-packaged. Nothing is off-the-shelf. What you get is a Generative AI capability that no competitor can replicate — because it’s built on what only you have.
01 Custom LLM Development
Who It’s For |
CTOs and VPs Engineering who need domain-specific language models and can’t afford hallucination in production. |
What We Build |
Design, train, and deploy custom large language models architected from the ground up for your domain, vocabulary, and output requirements. |
Key Deliverables |
Custom model architecture design → Training data curation and preparation → Full training or fine-tuning on proprietary datasets → Evaluation benchmarking against your use case → Production deployment with monitoring |
Why This Matters |
Generic LLMs don’t know your product, your terminology, your customer language, or your edge cases. A custom LLM does — from day one. |
02 LLM Fine-Tuning Services
Who It’s For |
Businesses with an existing foundation model (GPT-4, Llama, Mistral, Claude) that needs to perform better on specific tasks. |
What We Build |
Take a powerful foundation model and specialise it to your domain using curated, high-quality training data — dramatically improving accuracy and reducing hallucinations without the cost of training from scratch. |
Key Deliverables |
LoRA, QLoRA and full fine-tuning approaches → Domain-specific dataset creation and labelling → Hyperparameter optimisation for peak performance → Pre/post benchmarking with custom evaluation metrics → Deployment-ready fine-tuned model |
Why This Matters |
Off-the-shelf GPT-4 is a generalist. Fine-tuned GPT-4 is your expert. The accuracy difference in domain-specific tasks is not marginal — it’s the difference between a system your team trusts and one they ignore. |
03 RAG System Development (Retrieval-Augmented Generation)
Who It’s For |
Businesses with large internal knowledge bases — documents, manuals, FAQs, databases — that need an AI that can search and answer from them accurately. |
What We Build |
Build RAG pipelines that connect your LLM to your proprietary knowledge — databases, PDFs, wikis, product catalogs, CRM records — so every answer is grounded in your current, authoritative data. |
Key Deliverables |
Vector database setup (Pinecone, Weaviate, pgvector) → Document ingestion, chunking and embedding pipeline → Retrieval strategy (semantic, hybrid, keyword) → LLM integration with context-aware prompting → Answer grounding and hallucination reduction layer → Query understanding and intent routing |
Why This Matters |
RAG is the most practical fix for enterprise AI hallucination. It doesn’t require training a new model — it gives your existing LLM access to what it needs to know, when it needs to know it. |
04 Generative AI Application Development
Who It’s For |
Product leaders who want to ship GenAI-powered features — copilots, assistants, content engines — into their product or internal tools. |
What We Build |
End-to-end development of production-grade GenAI applications: from architecture design through backend, API layer, UI, and model integration. |
Key Deliverables |
AI copilot and assistant development → Content generation engines (text, code, summaries) → Conversational AI with multi-turn context memory → Text-to-image generation pipelines → Document intelligence apps (extraction, summarisation, Q&A) → Code generation and review assistants |
Why This Matters |
Most LLM wrappers are 200 lines of code. Production GenAI applications — with streaming, error handling, memory, multi-modality, and security — are engineering projects that require depth. |
05 Generative AI Consulting & Strategy
Who It’s For |
Business leaders who know they need GenAI but don’t know where to start — or who’ve started and are stuck. |
What We Build |
A structured engagement to identify your highest-ROI Generative AI use case, build the technical roadmap, evaluate build vs. buy, and create a deployment plan your team can execute. |
Key Deliverables |
AI readiness assessment → Use-case identification and prioritisation (by ROI and feasibility) → Technology stack recommendation → Build vs. buy vs. fine-tune analysis → Risk, compliance and data readiness review → 90-day GenAI deployment roadmap |
Why This Matters |
The most expensive GenAI mistake is building the wrong thing. Strategy consulting costs a fraction of a failed deployment — and it’s where we find the use cases that pay for everything else. |
06 GenAI Integration Services
Who It’s For |
Engineering teams who need GenAI capabilities embedded in existing products — CRM, ERP, helpdesk, analytics tools — without a rebuild. |
What We Build |
Seamlessly wire Generative AI capabilities into your existing software infrastructure via API-first integration — without disrupting what’s already working. |
Key Deliverables |
OpenAI, Anthropic, Google, Cohere API integration → Embedding GenAI into Salesforce, HubSpot, Zendesk, Slack → Custom API wrapper development → Prompt management and versioning systems → Streaming response and async job architecture → Rate limiting, cost monitoring, and fallback handling |
Why This Matters |
Ripping out your stack to add AI is expensive and risky. API-first integration means your existing tools gain AI capabilities — without the disruption or downtime. |
07 AI Model Fine-Tuning & Replication
Who It’s For |
Companies that want their own version of ChatGPT or DALL-E — a branded, domain-specific generative AI model built on proven architecture. |
What We Build |
Replicate proven generative AI architectures (GPT-style, DALL-E, Stable Diffusion) with custom training data to create proprietary models you own entirely. |
Key Deliverables |
ChatGPT-style conversational AI replication → DALL-E / Stable Diffusion image generation replication → GANs and VAEs for data synthesis and augmentation → Custom image generation for product design, creative work → Text-to-image and image-to-image models |
Why This Matters |
You don’t need to rent intelligence indefinitely from OpenAI’s API. You can own your model, control your data, eliminate per-token costs, and build a proprietary AI asset on your balance sheet. |
08 MLOps & GenAI Infrastructure
Who It’s For |
Engineering and data science teams who’ve built GenAI models that need reliable deployment, monitoring, and maintenance at scale. |
What We Build |
Production-grade ML infrastructure — training pipelines, model serving, performance monitoring, automatic retraining, and version control — so your GenAI solutions stay accurate and available. |
Key Deliverables |
Model training and retraining pipelines → Continuous performance monitoring (drift detection) → Model versioning and A/B testing infrastructure → Docker/Kubernetes deployment and scaling → Inference optimisation (latency, cost, throughput) → MLflow, TensorBoard, W&B integration |
Why This Matters |
A model that was 92% accurate on launch is 74% accurate six months later without monitoring and retraining. Models degrade. MLOps is how you stop the decay. |
Not Sure Where to Start with Generative AI?
Book a free 30-minute strategy session. We’ll map your highest-ROI GenAI use case, assess your data readiness, and tell you exactly what to build — and what to avoid.
What Happens When Your AI Is Built On Your Business
The difference between a generic LLM and a custom Generative AI system isn’t just technical — it shows up in every customer interaction, every internal operation, every decision your team makes.
Before: Generic AI | After: Custom GenAI | Business Impact | |
Customer Support |
Bot gives wrong answers. Agents correct it all day. |
AI knows your products, policies, and history. Resolves queries accurately. |
Support tickets handled without human intervention increase significantly |
Internal Knowledge |
Staff can’t find information. Ask colleagues. Waste hours. |
RAG-powered AI reads all internal docs. Instant, accurate answers. |
Time spent searching for internal information dramatically reduced |
Content Production |
Writer asks ChatGPT. Gets generic text. Rewrites everything. |
GenAI writes in your brand voice, trained on your content library. |
Content output scales without proportional headcount growth |
Data Analysis |
Analyst writes SQL. Waits. Formats report. Repeat. |
Natural language query to insight in seconds. Auto-generated reports. |
Insight-to-decision cycle compresses from days to minutes |
Product Recommendations |
Rule-based engine. Stale logic. Low conversion. |
GenAI model trained on purchase data, behaviour, context. |
Recommendation relevance and conversion improve materially |
Compliance Review |
Legal reads every document manually. Misses things. |
AI reviews contracts, flags clauses, extracts obligations. |
Review time compresses. Risk of missed obligations decreases |
Code Review & Generation |
Developer writes boilerplate. Code review is a bottleneck. |
Code copilot trained on your codebase generates and reviews. |
Developer velocity increases. Review cycle time decreases |
The Models, Frameworks, and Infrastructure We Build On
Your GenAI solution is only as good as the foundation it’s built on. Here’s what we use — and why we choose it.
Foundation Models
Fine-Tuning Methods
RAG & Retrieval
Model Training
Orchestration & Agentic
Image Generation
MLOps & Serving
Cloud Platforms
Data & Embedding
Generative AI Solutions Across Your Industry
Generic Generative AI delivers generic results. Domain-specific GenAI — trained and grounded in your industry’s language, compliance requirements, and workflows — delivers competitive advantage. Here’s what real business outcomes look like by sector.
Industry | What Custom GenAI Delivers |
Healthcare & Life Sciences |
AI that reads medical records, answers clinical questions, summarises patient history, assists diagnostics, and handles prior authorisation — HIPAA-compliant, on-premise deployable. |
Banking & Financial Services |
Fraud pattern generation for synthetic training data, regulatory document analysis, personalised wealth advisory, automated financial report drafting, and customer Q&A grounded in compliance policies. |
Legal |
Contract review, clause extraction, obligation mapping, legal research assistance, document summarisation, and client-facing Q&A — trained on your firm’s practice areas and precedent database. |
E-commerce & Retail |
Product description generation at scale, personalised recommendation copy, customer service agents trained on your catalogue, visual product search, and dynamic pricing narrative. |
Manufacturing & Supply Chain |
Technical documentation generation, maintenance manual Q&A, equipment fault diagnosis from sensor data narrative, supplier RFP generation, and quality assurance report automation. |
Real Estate |
Property description generation, market analysis reports, lease abstraction, tenant Q&A assistant, investment memo drafting, and property search via natural language. |
SaaS & Technology |
In-product AI copilots, developer code assistants trained on your codebase, customer onboarding assistants, documentation generation, and support ticket summarisation with suggested resolutions. |
HR & Talent |
Job description optimisation, candidate screening summary generation, employee onboarding knowledge assistant, HR policy Q&A, and performance review narrative generation. |
How We Build Your Generative AI System
From your first call to a production-deployed model. No black boxes, no surprise pivots, no demos that collapse under real load.
Week 1–2
Discovery & Scoping
We audit your data, workflows, and use case. Identify the right architecture (fine-tuned LLM, RAG system, or custom model). Define evaluation criteria and success metrics. Produce a technical specification and cost estimate.
Week 2–3
Data Readiness & Preparation
We assess your data quality, volume, and format. Clean, structure, and prepare training or retrieval datasets. Identify data gaps and address them with augmentation or collection strategies.
Week 3–6
Model Development & Training
Build, train, or fine-tune the model using the selected architecture. Iterative evaluation against domain-specific benchmarks. Multiple training runs with hyperparameter tuning until target accuracy is met.
Week 6–8
Integration & API Development
Build the API layer, backend infrastructure, and integration connectors for your existing stack. Connect to CRM, ERP, helpdesk, or custom systems. Implement streaming, caching, and error handling.
Week 8–10
Testing & Evaluation
Rigorous testing across real-world edge cases, adversarial inputs, and domain-specific evaluation sets. Load testing for production scale. Hallucination audits and output grounding verification.
Week 10–12
Deployment & Launch
Deploy to your chosen infrastructure — cloud, private cloud, or on-premise. Configure monitoring, logging, and alerting. Weekly sprint demos throughout so there are zero surprises at launch.
Post-Launch
Monitoring, Retraining & Support
Continuous performance monitoring with drift detection. Scheduled retraining cycles as your data evolves. Ongoing technical support. Roadmap planning for feature expansion.
What Makes Building With Shamla Tech Different
There’s no shortage of companies offering ‘Generative AI development services’. Here’s how the experience of building with us is different from the alternatives.
What Others Do | What We Do | Why It Matters |
Wrap your use case in the ChatGPT API, call it GenAI development |
Build or fine-tune a model specifically for your domain, data, and accuracy requirements |
A custom model outperforms a generic wrapper by margins that compound with every interaction |
Deliver a demo that works in controlled conditions |
Build for production from day one — real data volumes, edge cases, integration complexity |
Every wasted POC costs your team months. We scope for production, not demo. |
Send your data to third-party cloud APIs by default |
On-premise, private cloud, or hybrid deployment options — your data stays under your control |
Data sovereignty isn’t optional in healthcare, finance, and legal. We treat it as a constraint, not an afterthought. |
Disappear after delivery |
Monitoring, retraining, and ongoing support as your data and business evolve |
A model that was accurate at launch degrades. Long-term performance requires long-term partnership. |
Charge by the deliverable |
Flexible engagement: fixed-price, dedicated team, T&M, or consulting-only |
Different problems need different commercial structures. We adapt to how you work. |
Send a junior team after the sale |
Senior ML engineers, domain specialists, and dedicated project management throughout |
The people who scope your project are the people who build it. |
Choose How You Want to Work With Us
Model | Best For | How It Works | Billing |
Fixed-Price Project |
Clear scope, defined deliverables |
Agree on scope, architecture, timeline, and price upfront. Milestone-based delivery with weekly demos. No surprises. |
One-time or milestone payments |
Dedicated GenAI Team |
Complex, evolving, or long-running projects |
Full-time team of ML engineers, data scientists, and a PM works exclusively on your project. Scales up or down each month. |
Monthly retainer |
Time & Materials |
R&D-heavy work, unclear initial scope |
Pay for actual hours worked. Flexible scope — explore options, pivot, iterate freely without contract renegotiation. |
Hourly or weekly billing |
GenAI Consulting Only |
Not ready to build yet — need a clear roadmap first |
AI readiness assessment, use-case prioritisation, tech stack recommendation, build plan. No obligation to proceed with build. |
Project-based fee |
Ready to Build Generative AI That Actually Works for Your Business?
Book a free 30-minute consultation. We’ll review your use case, assess your data readiness, map the right GenAI architecture, and give you a realistic build plan — with timeline and cost estimate. No sales pitch. No commitment. Just a clear answer.
Frequently Asked Questions
What's the difference between using the ChatGPT API and building a custom Generative AI system?
The ChatGPT API is a general-purpose model trained on the internet. It doesn’t know your products, your customers, your domain terminology, or your specific edge cases. It also sends your data to OpenAI’s servers. A custom system — built with fine-tuning, RAG architecture, or purpose-trained models — knows your business specifically, performs more accurately on your tasks, operates at lower cost per inference, and keeps your data under your control. For most B2B use cases, the difference in accuracy and reliability is significant enough to determine whether the system gets used or abandoned.
Can you fine-tune models on our proprietary data without that data leaving our environment?
Yes. We offer on-premise training and fine-tuning workflows where your data never leaves your infrastructure. We can also work within your VPC (Virtual Private Cloud) on AWS, GCP, or Azure. Before any engagement begins, you’ll receive an NDA. Data handling protocols are established during the scoping phase and documented in the technical specification.
How do RAG systems reduce AI hallucination?
Retrieval-Augmented Generation works by connecting your LLM to a searchable index of your actual documents, data, and knowledge base. When a user asks a question, the system first retrieves the most relevant information from your knowledge base, then passes that information to the LLM as context. The model answers based on what it retrieved — not what it ‘imagined’. This dramatically reduces hallucination on domain-specific tasks while keeping the output fluent and natural.
What data do we need to get started, and what if our data isn't clean?
The right dataset depends on your use case. For fine-tuning: high-quality prompt-response pairs in your domain (as few as 500-1,000 well-constructed examples can be effective with modern LoRA approaches). For RAG: your documents, manuals, FAQs, and structured data in any format. For custom models: proprietary datasets curated for your task. If your data isn’t clean or well-structured, data preparation is part of what we do — it’s built into the engagement, not an extra.
How long does a generative AI development project take?
A focused GenAI application with a clear use case, adequate data, and defined integrations typically takes 8–12 weeks from kickoff to production deployment. LLM fine-tuning projects can be completed in 4–6 weeks. Complex enterprise platforms with multiple integrations and extensive testing can take 16–20 weeks. Every engagement begins with a scoping session that produces a personalised timeline.
What is the typical cost of a custom Generative AI project?
Cost depends on scope, data readiness, model architecture, and integration complexity. A GenAI consulting engagement typically starts from a few thousand dollars. LLM fine-tuning projects start from $10,000–$25,000. Full custom LLM development and production application builds range from $30,000 to $200,000+. We provide a transparent cost estimate after the free scoping session — before you commit to anything.
Which LLMs do you work with?
We work across the major foundation model families: OpenAI (GPT-4, GPT-4o), Meta (Llama 3.1, Llama 3.3), Google (Gemini 1.5, PaLM), Anthropic (Claude 3.5), Mistral, Mixtral, Falcon, Phi-3, and others. Model selection depends on your use case, budget, latency requirements, and data privacy needs. We’ll recommend the right model architecture during the scoping session — and explain why.
Can you integrate Generative AI into our existing product or tech stack?
Yes — integration-first is a core principle. We connect GenAI capabilities to your existing CRM, ERP, helpdesk, databases, and third-party tools via API. No need to rebuild what’s working. Common integrations include Salesforce, HubSpot, Zendesk, Slack, Notion, and custom internal tools. The AI layer enhances what you already have.
What compliance standards do you support?
We build with HIPAA, GDPR, and SOC 2 compliance requirements as a constraint from the start — not as an afterthought. This includes end-to-end data encryption, role-based access controls, audit trails, secure model serving environments, and data residency options. For regulated industries (healthcare, finance, legal), we review your specific compliance requirements during the scoping phase.
What happens after the model is deployed?
Deployment is the beginning, not the end. We provide ongoing monitoring for model accuracy drift, performance degradation, and anomalous outputs. We run scheduled retraining cycles as your data evolves. We offer technical support for integration issues, updates, and feature requests. We also run post-deployment roadmap sessions to identify the next highest-value AI opportunity in your business.
