Generative AI Development Services
Build powerful AI products with LLMs, GPT-4, RAG systems, and custom generative AI solutions for your business
Custom Generative AI Solutions for Modern Enterprises
Datasoft Technologies delivers end-to-end generative AI development services, from LLM integration and GPT-4 application development to Retrieval-Augmented Generation (RAG) pipelines, AI assistants, and fine-tuned language models built for your business.
Our generative AI engineers work with all major foundation models including OpenAI GPT-4, Anthropic Claude, Google Gemini, Meta LLaMA, and Mistral, selecting the right model architecture for your use case, budget, and data privacy requirements.
You might want an AI-powered customer support agent, an internal knowledge assistant, an AI content platform, or a fully custom LLM-based application. Whatever the use case, our team delivers production-ready generative AI solutions that scale.
AI Apps Built
LLMs Supported
Weeks to Launch
Private & Secure
Our Generative AI Development Services
Full-stack generative AI solutions from prototype to production
LLM Integration & Development
Integrate GPT-4, Claude, Gemini, and open-source LLMs into your products. Custom API wrappers, prompt engineering, and multi-model orchestration.
RAG Systems & Knowledge Bases
Build Retrieval-Augmented Generation systems that let AI answer questions from your private documents, databases, and knowledge repositories with high accuracy.
LLM Fine-Tuning
Fine-tune foundation models on your proprietary data to create domain-specific AI that speaks your business language and delivers highly relevant outputs.
AI Chatbots & Assistants
Build intelligent AI assistants for customer support, internal helpdesks, sales, and onboarding, powered by generative AI with memory and tool use.
AI Content Generation Platforms
Custom AI content tools for marketing copy, product descriptions, reports, and personalised communications at scale with brand voice consistency.
Private & On-Premise AI
Deploy open-source LLMs (LLaMA, Mistral) on your own infrastructure for complete data privacy. A strong fit for regulated industries and strict enterprise security policies.
Generative AI Technologies We Use
OpenAI GPT-4 / GPT-4o
Anthropic Claude
Google Gemini
Meta LLaMA
Mistral AI
LangChain / LlamaIndex
Vector Databases
Python / FastAPI
Why Generative AI Has Crossed Into Daily Production Use
Two years ago, generative AI was a research curiosity. Today, it writes the first draft of marketing copy, summarizes 30-page legal contracts in seconds, designs product images at scale, and powers customer-support agents that handle 70% of tickets without escalation. The question for every business is no longer "should we adopt generative AI?" It's "where does generative AI development earn its keep first, and how do we ship it without hallucinating into a lawsuit?"
At Datasoft Technologies, our generative AI development services span the full production stack: LLM-powered applications (GPT-5, Claude Opus, Gemini Pro, Llama 3, Mistral Large), Retrieval-Augmented Generation (RAG) for grounded answers on your private data, image and video generation pipelines (Stable Diffusion, Flux, DALL·E, Sora), AI agents and multi-agent workflows (LangGraph, CrewAI, AutoGen), and fine-tuning pipelines for domain-specific tone, format, or compliance.
Our generative AI engineering practice is opinionated about what actually works in production: RAG before fine-tuning for most enterprise use cases (cheaper, updates instantly, full citation trail), evaluation harnesses on day one (you can't improve what you can't grade), cost ceilings and model routing (route easy queries to Haiku-class models, hard ones to Opus), and guardrails for hallucination, prompt injection, and PII leakage, built in rather than bolted on after the first incident.
You might be a startup founder shipping a generative AI MVP, an SME automating high-volume content workflows, or an enterprise CTO deploying generative AI safely across 10,000 employees with audit trails. In every case, we treat the engagement as a production engineering project. That's the difference between a flashy demo and a feature your business depends on every day.
Our generative AI development services span every major use case: customer-facing AI assistants with grounded answers and human escalation, internal copilots for sales, support, and operations teams, document intelligence (extraction, summarization, classification, translation), creative pipelines (marketing image generation, ad variant production, product photography at scale), and autonomous agents that orchestrate multi-step workflows across your existing tools. Each one comes with the same engineering rigor: measurable success metrics, evaluation harnesses, observability, cost control, and a roadmap that survives contact with reality.
Time saved on content drafting, summarization, and document workflows
From kickoff to a production-grade generative AI feature with eval harness
Of accuracy control: grounding, evaluation, and human-in-the-loop
Generative AI Tech Stack
Model-agnostic, framework-pragmatic. We pick after a one-hour technical scoping, because the right stack depends on your latency budget, accuracy bar, cost ceiling, and data-privacy posture.
Foundation Models
- OpenAI GPT-4 / GPT-5
- Anthropic Claude (Opus, Sonnet, Haiku)
- Google Gemini Pro / Flash
- Meta Llama 3 / 4
- Mistral Large / Mixtral
- Open-source: Qwen, DeepSeek
Image & Video Models
- Stable Diffusion XL / 3
- Flux (text-to-image)
- DALL·E 3
- Sora (video gen)
- Runway Gen-3
- ControlNet, IP-Adapter
Orchestration
- LangChain & LangGraph
- LlamaIndex
- Haystack
- Semantic Kernel
- CrewAI / AutoGen (agents)
- vLLM (high-throughput)
Vector & Retrieval
- Pinecone
- Weaviate
- Qdrant
- pgvector (Postgres)
- Elasticsearch hybrid
- MongoDB Vector Search
Evaluation & Observability
- LangSmith / Langfuse
- Helicone
- Promptfoo / OpenAI Evals
- Weights & Biases
- Custom eval harnesses
- OpenTelemetry
Fine-tuning & Deployment
- LoRA / QLoRA fine-tuning
- Hugging Face Transformers
- AWS Bedrock / SageMaker
- Azure OpenAI / Vertex AI
- Modal / Replicate
- On-prem GPU clusters
Generative AI Pricing & Engagement
Three engagement models. Pick by scope clarity, expected iteration depth, and how much of the use case is still being shaped in real time.
| Model | Best For | Typical Range | Timeline |
|---|---|---|---|
| Generative AI MVP (Fixed) | Single-feature MVP: a chatbot, content generator, or RAG over a known corpus. Defined scope, measurable success metric. | $25K to $70K | 6 to 10 weeks |
| Time & Material | Multi-feature generative AI product, evolving scope, fine-tuning, evaluation pipelines, A/B testing. Most enterprise engagements. | $70K to $300K+ | 3 to 9 months |
| Dedicated Gen-AI Team | Long roadmap, multiple use cases, ongoing MLOps. AI engineer, ML engineer, MLOps and PM, embedded. | $18K to $45K / month | 6+ months |
Ranges depend on data volume, model choice, integration count, evaluation depth, and compliance scope. We provide a written estimate after a 30-minute discovery call, whether you choose to work with us or not.
Measurable Generative AI Outcomes
Generative AI value is measurable or it isn't real. Every engagement has a numeric success metric agreed in week one. Below are the ranges our clients consistently see.
Content drafting time
Marketing copy, sales emails, summaries, translations
Self-service ticket resolution
Grounded RAG support agents with citation enforcement
Document review time
Contract analysis, legal review, compliance checks
Creative asset throughput
Image generation pipelines for ads, product photography, design
Compliance, Safety & Responsible Generative AI
Generative AI without guardrails is a liability waiting to happen. We engineer safety in before code ships.
Hallucination Controls
RAG with citation enforcement, confidence thresholds, automated eval harnesses, regression alerts on every deploy.
Prompt Injection Defense
Input sanitization, output filtering, instruction-isolation, and jailbreak detection, all tested with red-team suites.
Privacy & Data Protection
GDPR, HIPAA, India DPDP Act, PII redaction in prompts and logs, no training on customer data without explicit consent.
EU AI Act Readiness
Risk classification (minimal / limited / high), transparency notices, human oversight workflows, model cards and data lineage docs.
IP & Copyright
Indemnified model usage where available, no use of copyright-flagged training data, output review for likeness/style risk.
Five Generative AI Mistakes We Help You Avoid
After dozens of generative AI deployments, the failure modes are predictable. These five kill more projects than any modeling problem.
Skipping the eval harness
If you can't grade the generative AI, you can't improve it. We ship every generative AI feature with a labeled test set and an automated evaluation pipeline that runs on every prompt or model change. Evaluation is the single highest-ROI engineering investment in any generative AI project. Without it, every change is a guess.
Trusting the model without grounding
LLMs are confident liars when ungrounded. We default to RAG with citation enforcement, confidence thresholds, and human-review queues for high-stakes outputs. The model has to point to its source, every time.
Letting token costs run wild
Generative AI cost balloons fast. We instrument cost-per-request, model-routing, caching, and per-workflow budgets from day one, so surprises never hit a CFO's desk. The first time an unmonitored loop costs five thousand dollars overnight is also the last time anyone trusts the AI initiative.
Building a demo, not a product
A working demo on three sample prompts is not a generative AI product. We design for the messy edge cases from week one: bad inputs, conflicting context, unusual prompts, and the long tail of weird user behavior. Production generative AI lives or dies on the 1% of inputs the demo never showed.
Ignoring prompt injection
Untreated prompt injection is a data exfiltration vector. We red-team every system with current jailbreak suites and harden inputs and outputs before launch: input sanitization, output filtering, instruction isolation, and runtime monitors that flag anomalous patterns. The cost of one prompt-injection breach reported in the press is bigger than every safety control combined, so we treat this as an architectural concern, not a bug-bash phase.
Generative AI Development FAQs
What are generative AI development services?
Generative AI development services involve building custom applications powered by large language models (LLMs) such as GPT-4, Claude, and Gemini. This includes AI chatbots, RAG systems, content generation tools, AI assistants, and fine-tuned models for specific business use cases.
How long does it take to build a generative AI application?
A basic generative AI application or chatbot can be built in 4 to 8 weeks. More complex systems like RAG pipelines, fine-tuned LLMs, or enterprise AI platforms typically take 2 to 4 months depending on data availability and integration complexity.
Which LLMs do you work with?
We work with all major LLMs including OpenAI GPT-4/GPT-4o, Anthropic Claude, Google Gemini, Meta LLaMA, and Mistral. We select the best model for your use case, budget, and data privacy requirements.
Can you build private generative AI solutions for enterprises?
Yes. We build fully private, on-premise or cloud-isolated generative AI solutions using open-source LLMs like LLaMA and Mistral, ensuring your data never leaves your infrastructure.
Should we use RAG or fine-tuning for our private data?
For most enterprise generative AI use cases, RAG wins. It's cheaper to build and operate, updates instantly when documents change, and gives every answer a citation trail. Fine-tuning makes sense when you need a specific tone, format, or domain language. Even then, a hybrid of fine-tuning plus RAG usually outperforms either alone. We recommend the path after a one-hour data review.
How do you control hallucinations in generative AI?
Three layers, all engineered together: (1) grounded responses via RAG with strict citation enforcement so every answer points to a source document, (2) automated evaluation harnesses that grade every response against a labeled test set with regression alerts on each prompt or model change, and (3) confidence-based routing that sends low-confidence answers to a human-review queue. Hallucination management is a measurable discipline, not a one-time fix.
How do you handle generative AI cost control?
From day one. We instrument cost-per-request, build model-routing layers (Haiku-class for easy queries, Opus-class for complex ones), implement caching for repeated questions, set token budgets per workflow, and surface daily cost dashboards. The first time a single prompt loop bills you $5,000 overnight, you'll wish you had this. We make sure clients never have that conversation.
Are you compliant with the EU AI Act and other AI regulations?
Yes. We classify every system per EU AI Act risk tiers (minimal, limited, high), implement transparency notices, human oversight workflows, model cards, and data lineage docs. We also align with NIST AI RMF, ISO 42001 readiness, India's DPDP Act, and HIPAA for healthcare AI. Audit teams should be able to review your generative AI without stopping the product.
Can you integrate generative AI with our existing tools?
Yes. We integrate generative AI into Salesforce, HubSpot, Zendesk, custom CRMs, Laravel/Django/Node back-ends, ERP systems, mobile apps, Slack, Microsoft Teams, and data warehouses (Snowflake, BigQuery, Redshift). Integration is most of the work, so we plan it as carefully as the model itself, with idempotent APIs, retry logic, observability, and clear rollback paths.
Generative AI Development Pricing
Real ranges for LLM apps and Gen-AI products we ship. Token costs billed pass-through at cost. Run the full cost calculator for your scenario.
$15K to $40K
4 to 8 weeks
- RAG over 1-2 sources
- OpenAI / Anthropic API
- Eval harness
- Demo UI
$40K to $150K
3 to 5 months
- Multi-source RAG
- Fine-tuning (LoRA / QLoRA)
- Eval + LangSmith / Promptfoo
- Cost-aware model routing
$150K+
6+ months
- Multi-tenant LLM platform
- Self-hosted on Bedrock / Vertex
- Prompt-injection defense
- SOC2-aligned controls
Ready to Build Your Generative AI Product?
Let's discuss your generative AI use case and build a production-ready LLM application for your business.