We design, build, and deploy custom AI systems — from RAG pipelines and LLM integrations to autonomous AI agents and fine-tuned models — using the right technology for your specific use case, data, and compliance requirements.
Every system we build is architected for enterprise scale, security, and long-term maintainability — not just a working proof-of-concept.
Connect large language models to your proprietary data so they answer using your documents, policies, and databases — not just generic training data. The foundation of most enterprise AI applications.
Connect AI capabilities to your existing enterprise systems — CRM, ERP, data warehouses, internal tools — with production-grade APIs, authentication, and error handling.
Build AI agents that perform multi-step tasks, make decisions, and interact with external APIs without constant human supervision — from research agents to fully autonomous workflow orchestrators.
Train AI models on your proprietary data to achieve higher accuracy on your specific tasks — medical coding, legal clause extraction, industry-specific classification — than generic models can deliver.
Build AI solutions that process and understand text, images, documents, audio, and structured data simultaneously — enabling use cases like visual inspection AI, document understanding, and voice interfaces.
Turn your historical data into forward-looking intelligence. Build demand forecasting models, churn prediction systems, anomaly detection engines, and NLP-powered analytics dashboards.
Every industry has unique data, compliance requirements, and performance benchmarks. Here's how we apply custom AI development across key verticals.
Ambient AI that listens to patient encounters and generates structured clinical notes, SOAP documents, and ICD-10 codes — reducing documentation time by 70–80% without touching existing EHR workflows.
Real-time ML pipelines that analyze transaction patterns, flag anomalies, and generate compliance-ready risk reports — processing millions of events per day with explainable AI outputs for your compliance team.
Integrate AI with your IoT sensor data to predict equipment failures before they happen, optimize maintenance schedules, and flag quality defects on production lines with computer vision.
RAG-powered document analysis systems that review contracts, identify key clauses, flag risks, and surface relevant case law — reducing per-document review time from hours to minutes.
ML-powered recommendation systems that personalize product discovery at scale, combined with demand forecasting models that optimize inventory levels and reduce stockouts across SKUs.
Embed AI capabilities directly into your product — intelligent search, natural language interfaces, automated onboarding, churn prediction, and product usage analytics powered by LLMs.
We select every tool based on your specific requirements — not on vendor partnerships or platform lock-in.
A four-phase process engineered to eliminate scope creep, production paralysis, and the 18-month death marches that plague traditional IT projects.
We interview your stakeholders, audit your data infrastructure, map integration constraints, and define success KPIs. Every engagement ends this phase with a signed scope document.
Our architects produce a detailed technical specification — model selection rationale, integration designs, data flows, security architecture — reviewed and approved by your team before coding starts.
Development proceeds in 2-week sprints. You see working demos after every sprint — not at the end. Feedback is incorporated immediately. Scope changes are managed formally with impact estimates.
We handle production deployment, set up monitoring dashboards, validate performance against KPIs, and transfer complete knowledge to your team. 90-day post-launch support included at no extra cost.
Measured outcomes across 150+ AI system deployments in production environments worldwide.
A Fortune 500 health system deployed an ambient clinical documentation AI. Medical staff documentation time dropped 80% in 8 weeks — without disrupting EHR workflows or requiring retraining.
A global fintech built a real-time fraud detection ML pipeline processing 10M+ transactions/month with 99.2% accuracy — recovering $4.2M in prevented fraud in the first quarter post-launch.
A top-10 law firm's RAG-powered contract analysis system reduced per-contract review time from 4 hours to 18 minutes — handling 300+ contracts per week automatically with full audit trails.
When should you build custom vs. buy? Here's how we help you think through it — and where custom AI wins decisively.
Everything enterprise decision-makers ask before working with us — answered directly and thoroughly.
Custom AI system development is the process of designing, building, and deploying artificial intelligence applications tailored to a business's specific requirements, data, and workflows. Unlike off-the-shelf AI tools, custom systems integrate with your existing infrastructure, meet your compliance requirements, and are optimized for your specific use cases — delivering higher accuracy, better ROI, and meaningful competitive differentiation that generic platforms cannot provide.
A focused AI system — such as a RAG-powered knowledge base or a document processing pipeline — typically takes 6–10 weeks from kickoff to production. A more complex system with multiple integrations and fine-tuned models takes 10–16 weeks. Enterprise platforms with multi-module architecture take 4–9 months. Our average time-to-production is 10 weeks — approximately 3× faster than traditional IT timelines — because we begin every engagement with a fixed, detailed scope and develop in 2-week sprints with working demos after each.
We are model-agnostic — we select the best model for each specific use case rather than forcing every project into one platform. We work with Anthropic Claude (excellent for reasoning, document analysis, and compliance-sensitive tasks), OpenAI GPT-4o (strong general-purpose and multimodal capabilities), Google Gemini (ideal for long-context tasks), and open-source models including LLaMA 3 and Mistral for use cases requiring on-premise deployment or full data sovereignty. Model selection is explained and justified in the technical specification before any development begins.
RAG (Retrieval-Augmented Generation) connects a large language model to your private data so it answers questions using your specific documents, policies, and databases — not generic training data. For enterprises, this is transformative: it means employees can query internal knowledge bases in plain English, customer support AI can answer product-specific questions accurately, and compliance systems can reference the exact policy document when flagging an issue. RAG is the foundation of most enterprise AI applications we build, and it eliminates the hallucination problem by grounding AI responses in your verified data sources.
Yes, 100%. All code, models, fine-tuning datasets, vector embeddings, prompt architectures, and system designs developed during your engagement are your exclusive intellectual property from day one. We retain no rights to reuse your systems, and we do not train our own models on your data. Full IP assignment is written explicitly into every contract we sign — not buried in the terms, front and center in the agreement.
Yes — integration with existing enterprise systems is one of our core competencies. We have built integrations with SAP, Oracle ERP, Salesforce, Microsoft Dynamics 365, ServiceNow, Workday, and dozens of proprietary legacy systems. Where direct API integration isn't available, we implement custom middleware layers and CDC (Change Data Capture) pipelines to keep AI models updated with real-time data from your operational systems. Integration architecture is mapped and approved in the technical design phase before development begins.
Costs depend on scope and team size. A focused AI system project (RAG knowledge base, document processing pipeline, custom chatbot) typically ranges from ₹15–40 lakh ($18,000–$50,000 USD). A complex system with multiple integrations, fine-tuned models, and production MLOps infrastructure typically ranges from ₹40 lakh–₹1.5 crore ($50,000–$180,000 USD). After a free 45-minute discovery call, we provide a detailed fixed-scope quote with itemized deliverables — so there are no surprises. Ongoing inference and hosting costs are modeled separately in a Total Cost of Ownership analysis.
Yes — most of our clients start from zero AI infrastructure. Our AI readiness assessment identifies what data assets you have, what team skills exist, and what infrastructure gaps need to be addressed. We then build the necessary data pipelines, cloud infrastructure, and AI systems together, and transfer knowledge to your team as we go. You don't need a data science team, a data lake, or any prior AI experience to work with us. We bring everything needed and make you self-sufficient by the end of the engagement.
Tell us what you're trying to build. In our first call, a senior AI architect will assess your use case, identify the right architecture, and give you an honest estimate of timeline and cost.
Direct with a senior AI architect. No sales scripts.
Architecture outline, timeline, and cost estimate within 48 hours.
From contract signing to first sprint kickoff in under 14 days.
We respond within 4 business hours.
Your information is protected and never shared.