Artificial Intelligence & Machine Learning at eCorpIT is the design, engineering, deployment, and ongoing operation of AI systems — custom agents, generative applications, retrieval-augmented generation (RAG), computer vision, voice AI, predictive models, and the ML operations infrastructure that keeps them running in production.
AI & Machine Learning Services
AI agents and machine learning, built for production.
eCorpIT builds custom AI agents, generative AI applications, RAG systems, computer vision, voice AI, and ML operations — on OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, and private models. CMMI Level 5 process discipline. Full IP transfer at handover.
- CMMI Level 5
- Production AI shipped, not demos
- NDA before any technical discussion
- Source code and AI models 100% yours
- Deployed into your cloud account, not ours
What AI & ML means at eCorpIT
The design, engineering, deployment, and operation of AI systems that survive production.
Four sub-disciplines compose the practice
Most engagements draw on two or three. Common patterns:
- AI Agent RAG Customer-support agent grounded in your docs
- Computer Vision LLM Document-processing pipelines
- Predictive ML ML Ops Recommendation systems in production
What we build
Pick a sub-discipline. See exactly what we ship.
Four sub-disciplines compose this practice. Most engagements draw on two or three. Open one to see the production-grade definition, what we deliver, the tech we work with, and use cases we've shipped.
AI Agents & Generative AI Applications
AI Agents at eCorpIT are autonomous software systems that handle multi-step workflows — customer support, sales qualification, document processing, internal operations — using large language models, tool calling, and structured memory. Generative AI Applications wrap those agents in domain-specific user interfaces.
What we deliver
- Custom AI agents — autonomous agents for customer support, SDR work, internal operations, multi-step reasoning. Built on Claude, GPT-4, Gemini, Llama, or your private LLM.
- Conversational AI applications — chat interfaces, voice interfaces, multi-channel deployment (web, mobile, WhatsApp, Slack, Teams).
- Document AI and content generation — invoice extraction, contract review, KYC parsing, claims processing, long-form content generation.
- Code copilots and developer assistants — internal AI tooling, code review automation, test generation.
- Image and video synthesis — generative image pipelines for marketing, design, product visualization.
- Agentic workflow automation — multi-step agent orchestration across SaaS tools using LangGraph, CrewAI, or custom frameworks.
Tech stack (12)
- Anthropic Claude
- OpenAI
- Google Gemini
- Meta Llama
- Mistral
- LangChain
- LangGraph
- LlamaIndex
- CrewAI
- AWS Bedrock
- Azure AI Foundry
- GCP Vertex AI
Use cases we've shipped
AI customer support agents, AI sales qualification bots, AI document processing systems, AI internal knowledgebase assistants, AI lead enrichment workflows, AI content automation pipelines, AI workflow agents across SaaS tools.
LLM Integration & RAG Systems
LLM Integration & RAG at eCorpIT covers retrieval-augmented generation systems, large language model fine-tuning, vector database architecture, embeddings pipelines, and the data infrastructure that grounds AI responses in your private knowledge — so the AI answers from your documentation, not from the open internet.
What we deliver
- Retrieval-augmented generation (RAG) — private RAG over your documents, codebase, support tickets, or internal wiki. Chunking strategy, hybrid retrieval (semantic + keyword), reranking, citation handling.
- Vector database architecture — Pinecone, Weaviate, Qdrant, pgvector, Milvus. Embedding pipeline design, indexing strategy, hybrid search.
- LLM fine-tuning — domain-specific model training. LoRA, QLoRA, full fine-tuning where justified. Open-source (Llama, Mistral) or proprietary fine-tunes.
- Prompt engineering and management — versioned prompt libraries, A/B testing, evaluation harnesses.
- Private and on-premise LLM deployment — self-hosted Llama, Mistral, or Phi for data-residency or air-gap clients.
- Multi-LLM orchestration — route the right query to the right model: Claude Opus for complex reasoning, Haiku for low-latency, Gemini for long context, fine-tuned models for specialized tasks.
Tech stack (14)
- Pinecone
- Weaviate
- Qdrant
- pgvector
- Milvus
- Chroma
- LangChain
- LlamaIndex
- Hugging Face
- Anthropic Claude
- OpenAI
- Mistral
- vLLM
- Ollama
Use cases we've shipped
Customer support RAG over a 10,000-document knowledge base, internal HR/legal/sales "ask the company" assistants, code search and engineering Q&A, compliance-document retrieval and answer generation, long-form research assistants for analyst teams.
Computer Vision & Voice AI
Computer Vision & Voice AI at eCorpIT covers image and video understanding (object detection, OCR, defect inspection, video analytics, biometrics) and voice interfaces (voice agents, transcription, real-time voice translation, multilingual voice commerce).
What we deliver
- Computer vision systems — object detection, defect inspection, biometric and identity systems, sports/event video analytics, visual QA for manufacturing.
- OCR and document understanding — invoice extraction, ID parsing, KYC, contract analysis, handwriting recognition, form digitization.
- Voice AI agents — voice agents for customer service, voice-driven workflows for field staff, hands-free interfaces for healthcare and logistics.
- Speech-to-text and text-to-speech — multi-language transcription, real-time captioning, voice synthesis for accessible interfaces.
- Multimodal AI — vision-language models that reason across images, video, and text together. Product search, content moderation, visual Q&A.
Tech stack (13)
- OpenCV
- PyTorch
- TensorFlow
- YOLO
- Detectron2
- MediaPipe
- Tesseract
- AWS Rekognition
- Azure Vision
- GCP Vision AI
- ElevenLabs
- OpenAI Whisper
- Deepgram
Use cases we've shipped
On-device document scanning in mobile apps, manufacturing defect inspection, sports event highlight extraction, healthcare imaging support, multilingual voice customer support.
Predictive Analytics & ML Operations
Predictive Analytics & ML Operations at eCorpIT covers traditional machine learning (forecasting, classification, recommendation, anomaly detection) plus the operational infrastructure — model deployment, monitoring, drift detection, evaluation, and governance — that keeps ML systems trustworthy in production.
What we deliver
- Forecasting and demand planning — sales, inventory, traffic, financial forecasting. Prophet, NeuralProphet, or custom architectures.
- Recommendation systems — collaborative filtering, content-based, hybrid, LLM-augmented recommendation engines.
- Anomaly and fraud detection — real-time anomaly detection for fraud, security, and operational monitoring.
- Churn and propensity modeling — customer LTV, churn prediction, upsell propensity, conversion modeling.
- ML Ops infrastructure — deployment pipelines (SageMaker, Vertex AI, Azure ML), versioning, A/B testing, drift monitoring (Arize, WhyLabs), evaluation harnesses.
- AI governance and compliance — audit trails, model cards, bias auditing, EU AI Act / NIST AI RMF / RBI Responsible AI alignment.
Tech stack (14)
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- LightGBM
- Prophet
- MLflow
- Weights & Biases
- Arize AI
- WhyLabs
- AWS SageMaker
- Azure ML
- GCP Vertex AI
- Kubeflow
Use cases we've shipped
Retention and LTV modeling, demand and inventory forecasting, real-time fraud detection, ML-Ops backbone for multi-model production deployments, governance-aligned model registry for regulated industries.
Full tech stack
The models, frameworks, and platforms we ship in production.
Foundation models
- Anthropic Claude
- OpenAI
- Google Gemini
- Meta Llama
- Mistral
- DeepSeek
Frameworks & orchestration
- LangChain
- LangGraph
- LlamaIndex
- CrewAI
- AutoGen
- Semantic Kernel
Vector databases
- Pinecone
- Weaviate
- Qdrant
- pgvector
- Milvus
- Chroma
ML platforms
- AWS Bedrock
- AWS SageMaker
- Azure AI Foundry
- Azure ML
- GCP Vertex AI
- Hugging Face
- PyTorch
- TensorFlow
ML Ops & evaluation
- MLflow
- Weights & Biases
- Arize AI
- WhyLabs
- Evidently AI
- Kubeflow
- Langfuse
- Phoenix
Vision & voice
- OpenCV
- YOLO
- Detectron2
- MediaPipe
- OpenAI Whisper
- ElevenLabs
- Deepgram
From use case to production
A 5-step framework that survives the AI demo-to-production gap.
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Week 1
AI Discovery & Use-Case Validation
Free 30-minute call. We map your use case against three questions: Does the data you have support this? Does the latency and cost budget close? Is the failure mode acceptable to your business? Many AI projects die here — and that's a feature, not a bug. Within 5 working days, you receive a one-page AI strategy doc with a recommended model, retrieval approach, indicative cost, and a delivery roadmap.
- 30-min call
- Use-case validation
- One-page strategy doc
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Weeks 1–2
Data Assessment & Feasibility Build
Before building anything client-facing, we audit your data: volume, quality, sensitivity, accessibility. For RAG, we test retrieval against a sample document set. For ML, we benchmark feasibility on a held-out test set. Outcome: a go / no-go memo with quantitative confidence in the use case.
- Data audit
- Retrieval / ML benchmark
- Go / no-go memo
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Weeks 2–10
Build (Two-Week Sprints, Eval-Gated)
Senior AI engineers paired with prompt engineers and data scientists. Every sprint ends with an evaluation run — accuracy, latency, cost, safety, hallucination rate — against a versioned eval set. We don't trust vibes. We trust the eval harness.
- Two-week sprints
- Versioned eval set
- Senior-only team
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Pre-launch
Guardrails, Governance, and Hardening
Hallucination guardrails, prompt-injection defenses, PII redaction, output filtering, audit trails, model cards. Compliance alignment for EU AI Act, NIST AI RMF, India's emerging AI governance framework. Red-team testing before production.
- Guardrails + PII
- Audit trails + model cards
- Red-team testing
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Launch → ongoing
Deploy & Monitor (ML Ops)
Deployed into your cloud account, not ours. Drift monitoring, performance monitoring, cost monitoring. Monthly model-quality reviews. Retraining or prompt iteration triggered by quantitative thresholds, not gut feel.
- Your cloud account
- Drift + cost monitoring
- Monthly reviews
How to engage us
Six Engagement Models. Match the model to the work.
Staff augmentation is intentionally not on this list. Every engagement below is an outcome we take responsibility for, not a developer rented by the hour.
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Best for · First engagement · AI/concept validation
Discovery & Prototype Sprint
A short, fixed-fee sprint that de-risks a bigger decision. We deliver a working prototype, a clean technical architecture, and a costed roadmap — so you can commit to a larger build with evidence, not speculation. The most common entry point before a Fixed-Scope, Pod, or Partnership engagement.
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Best for · Clearly defined initiatives
Fixed-Scope Project
Software builds, AI implementations, infrastructure migrations, security audits. One quote, one deadline, one delivery.
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Best for · Ongoing product development
Dedicated Product Pod
Monthly retainer. A senior pod operates as your engagement team — same Slack, same standups, same OKRs.
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Best for · Category-defining products
Long-Term Product Partnership
Multi-quarter or multi-year roadmap. eCorpIT becomes your engineering partner organization.
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Best for · 24×7 operations
Managed Services
Cloud, security SOC, network NOC, data center, application support. Predictable monthly retainer; SLA-backed.
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Best for · Early-stage founders
Fractional CTO + Engineering Pod
Senior strategic and engineering leadership on a monthly retainer. For founders without a technical co-founder.
Real AI work
Most AI work is under NDA. Here's what we can talk about.
AI shows up across our ERP, LMS, and portal work for multiple clients — some public, most under NDA. Two engagements we can name:
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Healthtech · Live
TrustingMinds — HealthCare ERP
Flutter healthcare patient and doctor digital health platform with AI-supported workflows for appointment management, prescription support, and patient progress monitoring.
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Finance media · Live
Global Banking & Finance Review
Publishing architecture rebuilt on Next.js + Sanity with AI assisting editorial workflow, content discovery, and recommendation across a high-scale finance media platform.
Reference implementations and shipped use-case patterns
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AI customer support agents
Multi-channel — web, mobile, WhatsApp.
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AI document processing
Invoices, KYC, contracts, parsing pipelines.
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AI lead-qualification
Sales enrichment and outbound workflows.
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Internal knowledgebase RAG
"Ask the company" over private docs.
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Workflow automation
Multi-step agents across SaaS tools.
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CV document scanning
On-device scanning embedded in mobile apps.
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Voice agents
Customer service and field operations.
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Predictive analytics
Retention, demand forecasting, risk modeling.
Industries we serve
Deep Experience Across Business Verticals
We map our practice mix to the operating reality of each sector. Hover any card to see how we deliver in that vertical.
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Healthcare
Clinical platforms, telemedicine, patient apps, EHR integration, compliance-aligned design.
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Education
LMS, AI tutors, assessment platforms, school management.
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Manufacturing
IIoT platforms, predictive maintenance, computer-vision QA, MES.
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Sport and Event
Sports governance platforms, scoring and rankings, ticketing, event operations.
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Retail
D2C platforms, omnichannel commerce, AI personalization.
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Consumer & Distribution
Distributor portals, B2B catalogs, route-to-market platforms.
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Media
OTT, finance media, content recommendation, editorial systems.
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Startups & Enterprises
MVP delivery for founders; modernization and AI integration for enterprise teams.
Why eCorpIT for AI development
Nine commitments. AI delivery, not AI theater.
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We ship production AI, not demos.
Most AI vendors will hand you an impressive prototype that breaks the first time real users get hold of it. We build for the second half of the AI journey — evaluation harnesses, hallucination guardrails, drift monitoring, governance. Audit-ready from day one.
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CMMI Level 5 process discipline applied to AI delivery.
Every AI system runs under CMMI Maturity Level 5 process controls. Quantitative management. Versioned evaluation. Documented retraining triggers. Rare for AI shops — most operate in research-lab mode. Enterprise and regulated-industry buyers can sign off without compliance friction.
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Model-agnostic by default.
No contractual loyalty to OpenAI, Anthropic, Google, or any other model provider. We pick the right model for your use case — Claude for complex reasoning, GPT-4 for general-purpose, Gemini for long context, fine-tuned Llama for specialized tasks, on-premise models for data-residency. Your stack, not ours.
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Models, prompts, eval sets, and code are 100% yours.
All model weights, prompts, evaluation datasets, retrieval indexes, and infrastructure-as-code are committed to your repository and deployed into your cloud account. You can switch vendors at any time. We don't lock you in.
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Your data trains your models. Nobody else's.
We never use your data, prompts, or evaluation sets to train models for other clients or third-party vendors. Zero-retention API contracts with model providers where available. Inference logs encrypted, role-scoped, and lifecycle-controlled. Your data stays your data — contractually and architecturally.
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Cost telemetry from day one.
Token budgets, per-user rate limits, model-tier fallbacks, and per-feature cost dashboards shipped with every production AI system. Runaway inference spend is the most common production AI failure — we engineer it out before launch, not after the first surprise invoice.
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Cloud-partner alignment.
AWS Partner, Microsoft Partner, Google Partner — so AI deployment on AWS Bedrock, Azure AI Foundry, or GCP Vertex AI gets you partner-tier support and procurement-friendly contracts.
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NDA before any technical conversation.
Mutual NDA signed before we receive specs, data samples, or architecture details.
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Milestone-based payments.
Pay as we deliver. Not lump-sum upfront. AI projects fail unpredictably — milestone payments protect you against that risk.
Transparent AI development pricing
Typical engagement ranges. Real numbers, not "contact us" gates.
Final pricing depends on data complexity, model choice, evaluation rigor, and integration scope. These are indicative ranges based on our typical engagements. INR pricing and GST invoicing available for India-based clients.
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AI Agent / Chatbot
$5K – $25K
3–8 weeks
Single-purpose agent with grounded responses and basic guardrails.
Fixed-scope
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Most popular RAG System (private docs)
$8K – $30K
4–10 weeks
Document ingestion, vector indexing, retrieval, citation handling.
Fixed-scope
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Generative AI Application
$10K – $50K
6–14 weeks
Full UI, multi-step workflow, evaluation, deployment.
Fixed-scope
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Computer Vision System
$15K – $60K
8–16 weeks
Custom model training, edge or cloud deployment, monitoring.
Fixed-scope
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Voice AI System
$10K – $40K
5–12 weeks
Voice agent with STT, TTS, and conversation management.
Fixed-scope
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Predictive ML Model
$5K – $30K
3–10 weeks
Forecasting, classification, recommendation, or anomaly detection.
Fixed-scope
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ML Ops Engagement
Project + retainer
Ongoing
Initial setup as a project; monthly retainer for ongoing operations.
Project / retainer
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AI Strategy Consulting
Fixed or retainer
2–4 weeks
Fixed-scope diagnostic or monthly strategic retainer.
Fixed / retainer
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Dedicated AI Pod
Monthly retainer
Ongoing
Senior AI engineer + prompt engineer + data scientist + ML Ops engineer.
Monthly retainer
Milestone-based payment terms across all AI engagement types. NDA signed before any technical conversation. Equity-friendly arrangements for select early-stage founders.
Frequently asked questions
AI development, answered.
No matches. Try a different keyword or contact us directly.
What kind of AI does eCorpIT build?
eCorpIT builds custom AI agents, generative AI applications, retrieval-augmented generation (RAG) systems, large language model integration, computer vision systems, voice AI agents, predictive ML models, and ML operations infrastructure — on OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, and private/on-premise models.
Does eCorpIT use OpenAI, Anthropic, or open-source models?
All three. eCorpIT is model-agnostic and picks the right model per use case — Claude for complex reasoning, GPT-4 for general-purpose tasks, Gemini for long context, fine-tuned Llama or Mistral for specialized or on-premise needs. Your stack, not ours.
Does eCorpIT build RAG systems?
Yes. Retrieval-augmented generation over private documents is one of our most-shipped engagement types. We design chunking strategy, build hybrid retrieval (semantic + keyword), implement reranking and citation handling, and ground responses in your data — not in the open internet.
How much does an AI agent cost?
Single-purpose AI agents typically cost USD $5,000–$25,000 over 3–8 weeks. RAG systems over private documents typically cost USD $8,000–$30,000 over 4–10 weeks. Full generative AI applications with UI and evaluation harness typically cost USD $10,000–$50,000 over 6–14 weeks.
How long does it take to build an AI system?
Typical AI agents and RAG systems: 4–10 weeks. Custom computer vision or full generative AI applications: 8–16 weeks. Complex multi-agent platforms: 12–20 weeks. Final timeline confirmed in the discovery phase.
Can eCorpIT integrate AI into our existing systems?
Yes. AI integration into existing CRMs, ERPs, customer support platforms, internal tools, and SaaS stacks is one of our most-common AI engagement types. We connect AI capabilities to your existing data sources, workflows, and user interfaces.
Will the AI models, prompts, and code belong to me?
Yes. All model weights (including fine-tuned models), prompts, evaluation datasets, retrieval indexes, code, and infrastructure-as-code are owned by you. Deployed into your cloud account. Committed to your GitHub or GitLab. You can switch vendors any time.
Does eCorpIT handle AI governance and compliance?
Yes. We deliver AI systems aligned with EU AI Act, NIST AI Risk Management Framework, RBI Responsible AI guidelines (for Indian financial services), and India's emerging AI governance framework. Audit trails, model cards, bias auditing, and red-team testing are standard before production.
Does eCorpIT do ML Ops?
Yes. ML deployment pipelines (SageMaker, Vertex AI, Azure ML), model versioning, drift monitoring (Arize, WhyLabs, Evidently), evaluation harnesses, and retraining workflows. We build for the second half of the AI lifecycle, not just the prototype.
Can eCorpIT deploy AI on-premise or in our private cloud?
Yes. We deploy private and on-premise LLMs (Llama, Mistral, Phi, DeepSeek) for clients with data-residency, compliance, or air-gap requirements. We can also deploy into your AWS, Azure, or GCP account.
Is eCorpIT a CMMI-certified AI development company?
Yes. eCorpIT is CMMI Level 5 (Maturity Level 5), the highest level in the CMMI for Development model. Every AI system we ship runs under CMMI Level 5 process controls.
Does eCorpIT build AI-powered mobile apps?
Yes. AI mobile apps — combining LLM agents, on-device ML, OCR, voice, and computer vision — are one of our most-shipped engagement types. See the dedicated landing page at /services/ai-mobile-application-development-services/ for AI mobile app pricing, tech stack (Flutter, React Native, Claude, GPT, Gemini), and shipped use cases including the healthcare patient/doctor platform.
How do I start an AI project with eCorpIT?
Fill the project estimate form on this page or book a 30-minute discovery call. Within 24 working hours, a senior AI architect responds with a recommended model, retrieval approach, indicative pricing range, and a delivery timeline. NDA available on request.
Have an AI use case? Let's price it.
Free 24-hour estimate from a senior eCorpIT AI architect. Agent, RAG system, computer vision, voice AI, predictive model, or full AI platform — you walk away with a recommended model, retrieval approach, indicative cost, and a realistic delivery timeline. No commitment.