How do you reduce hallucinations in GenAI applications?

We use targeted retrieval, source grounding, structured prompts, confidence rules, and continuous evaluation. For RAG systems, our generative AI model development services refine chunking, metadata, hybrid search, re-ranking, and citation logic. We also add fallbacks when source context is missing.

How do you manage the cost of LLM-based applications?

We model usage, token volume, model pricing, traffic estimates, and retry patterns upfront. Cost control relies on prompt/response caching, dynamic model routing, context compression, smaller models for simple tasks, rate limiting, and live usage monitoring.

Can you improve an existing GenAI prototype?

Yes. We audit existing prototypes for retrieval quality, latency, prompt structure, cost, and integrations. Our generative AI development services company then redesigns RAG pipelines, adds prompt version control, optimizes context windows, improves structured outputs, and sets up automated regression testing.

How do you handle prompt engineering for production systems?

We treat prompts as production code. This is why our generative AI solution development process includes version control, structured output formatting, automated evaluation suites, rollback capabilities, and live performance tracking to maintain stability throughout.

What types of GenAI applications can you build?

We build custom GenAI solutions for enterprise knowledge Q&A, document processing, semantic search, automated triage, content generation, data extraction, summarization, and quality monitoring. We tailor every generative solution to your specific accuracy, latency, and security needs.

Generative AI Development

We Ship GenAI
Features That Actually
Work in Production.

Every team is experimenting with GenAI, but only a few are becoming successful. Which side are you on?

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CMMI
Level 3
HIPAA
Compliant
AWS
Partner Network
ISO Certified
For Data Quality & Security
SOC 2
Type II Compliant

Why 90% of Generative AI Pilots Don’t Make it to Production

Demos are impressive, but they operate in controlled environments where edge cases, latency, security boundaries, and enterprise scale don't exist.

Accuracy is often aspirational but rarely delivered in production.
Real-world latency requirements kill more GenAI features than hallucinations.
Prompt engineering without version control adds to the technical debt.
LLM inference costs compound faster than teams expect.

Our generative AI developers build for the messy, high-scale reality after the demo ends.

Enterprise-Grade Generative AI Development Services

We start with your latency budget, cost constraints, and accuracy requirements, then design your Generative AI solutions. With us, you get end-to-end support, from an expert consultation to model maintenance long after deployment.

Generative AI
Consulting
We provide strategic advisory for enterprises evaluating genAI, its use cases, and the resulting ROI. We leverage structured frameworks like Gartner’s AI Opportunity Radar and McKinsey’s GenAI Value Matrix to roadmap pilots, select foundation models, and align AI investments with clear business outcomes.
Custom Generative AI
Application Development
End-to-end generative AI app development that turns ideas into production-ready products. We engineer full-stack AI interfaces with generative capabilities, high-throughput back-end APIs with FastAPI and vLLM, and semantic caching pipelines deployed securely on Docker/Kubernetes for your specific domain.
Generative AI Model
Development
As a dedicated generative AI service provider, we design, train, and validate custom architectures from scratch on your proprietary business data. We build tailored Transformer, Diffusion, and Multimodal models using leading frameworks like PyTorch, JAX, and DeepSpeed distributed training frameworks.
LLM
Development
Specialized LLM development for AI Agents, chatbots, and copilots. Our generative AI experts handle LLM pre-training, instruction tuning, LoRA/QLoRA PEFT, RLHF/DPO, and inference optimization (TensorRT-LLM, llama.cpp) across all open-weight and commercial providers.
RAG
Development
We design high-precision Retrieval-Augmented Generation systems using advanced RAG techniques, including hybrid search (dense + sparse), ColBERT re-ranking, and parent-document retrieval. Using vector stores like Pinecone, Qdrant, and pgvector, we ensure grounded, citation-aware outputs.
Generative AI
Integration
Plug generative AI into your existing stack using event-driven architectures, REST/gRPC APIs, and Model Context Protocol (MCP). Our team ensures seamless integration with CRMs, enterprise data warehouses (Snowflake, BigQuery, etc), and internal microservices.
Autonomous AI Agents
& Workflows
Extend foundation GenAI models from content generation into task execution. We build agentic workflows that leverage LLMs as reasoning engines, enabling systems to run multi-step tasks, use external tools, and query live enterprise databases. Built with frameworks like LangGraph and CrewAI for controlled, stateful execution.
Generative AI Model
Fine-Tuning
Our generative AI experts improve domain accuracy and reduce inference costs by fine-tuning models on your internal data. We apply Parameter-Efficient Fine-Tuning (PEFT), Supervised Fine-Tuning (SFT), and quantization (AWQ, GPTQ) to adapt open-source models (Llama, Mistral) to your industry terminology and compliance needs.
GenAI Security &
Governance
Ensure safety, compliance, and risk mitigation across your AI lifecycle. We implement real-time guardrails with NeMo Guardrails and Llama Guard, data anonymization pipelines, OWASP LLM Top 10 vulnerability mitigations, red teaming, and automated compliance tracking in all GenAI solutions. Our compliance framework is aligned with global data privacy and AI regulations, including GDPR, CCPA, and the EU AI Act.
Model Upgrade & Maintenance
Our generative AI services provide continuous model upgrades and maintenance, including drift monitoring, retraining pipelines, and security patches. With our ongoing support, your gen AI solutions stay reliable and performant over time.

Let’s Build Enterprise GenAI You Can Audit,
Secure, and Scale

From zero-trust architectures to automated GenAI guardrails, we engineer production-ready systems with built-in security, compliance, and full operational control.

Book a GenAI Consultation

Why Engineering Teams Choose Our Generative AI Services

Feature	Other Agencies	In-House Build	Our Approach
Model Evaluation Framework	Strategy only	Ad hoc testing	Built before any model is selected
Latency Engineering	Rarely modelled	Discovered in staging	Budgeted and tested from day one
Prompt Management	Often not included	Scattered/undocumented	Versioned, tested, production-ready
GenAI Governance & Security	Retrofitted post-launch	Limited to basic API keys	Guardrails, red teaming, and compliance baked in
Cost Modelling	Rarely included	Discovered post-launch	Modeled for each feature & monitored live
RAG vs Fine-Tune Decision	Vendor-influenced	Determined by capacity	Use-case driven, honest recommendation

Model Evaluation Framework
Other Agencies
Strategy only
In-House Build
Ad hoc testing
Our Approach
Built before any model is selected
Latency Engineering
Other Agencies
Rarely modelled
In-House Build
Discovered in staging
Our Approach
Budgeted and tested from day one
Prompt Management
Other Agencies
Often not included
In-House Build
Scattered/undocumented
Our Approach
Versioned, tested, production-ready
GenAI Governance & Security
Other Agencies
Retrofitted post-launch
In-House Build
Limited to basic API keys
Our Approach
Guardrails, red teaming, and compliance baked in
Cost Modelling
Other Agencies
Rarely included
In-House Build
Discovered post-launch
Our Approach
Modeled for each feature & monitored live
RAG vs Fine-Tune Decision
Other Agencies
Vendor-influenced
In-House Build
Determined by capacity
Our Approach
Use-case driven, honest recommendation

Our generative AI app development company doesn’t recommend fine-tuning when RAG solves it. It doesn’t recommend GPT-4 when a smaller model is sufficient. Our recommendations are only driven by your requirements, not by vendor relationships.

GenAI Applications We've Shipped to Production

See all case studies

Solved an acute problem

Ten days to say yes, while fintechs said it in one

A commercial lender's underwriters spent 20–30 hours per loan file on manual data entry. We built a custom-trained document processing and workflow platform on AWS.

35%

higher throughput

99.5%

extraction accuracy

Read the case study

Rebuilt a broken core system

Only 1 in 10 searchers found what they wanted

A bath & lighting brand in 55+ countries had site search converting just 10–15% of searchers. We trained a transformer ranking model on their own catalog and query logs.

80%

more accurate search

72%

higher conversion

Read the case study

Found the bigger problem

Hired for maintenance. Found a platform.

An eCommerce retailer brought us in for nopCommerce upkeep. The real bottleneck was inventory and pricing scattered across five channels — so we built the fix.

60%

increase in sales

~90%

tasks automated

Read the case study

Provided a dedicated engineering pod

Reimagined their product thrice, without a single handover

A core retail POS platform had to outgrow three technology eras without losing what made it work. The same embedded pod carried it from CGI to PHP to AWS over 15 years, so no knowledge was ever lost in handover.

15+ years

Zero re-tendering and handovers

Read the case study

Tech Stack We Use

Web Development
Mobile App Development
Back-End, APIs, & Databases
AI, ML & Generative AI
Data Engineering & Analytics
Data Annotation & Labeling
Cloud, DevOps & FinOps
QA & Testing