We build agentic AI systems, RAG pipelines, LLM-powered applications, and enterprise AI integrations. Our team has trained models for Apple, Alibaba, and Oracle we know what production AI actually requires.
DigiChum Infotech has provided RLHF (Reinforcement Learning from Human Feedback) resources to train and align large language models for enterprise clients including Apple, Alibaba, and Oracle. This isn't theoretical AI work we know what it takes to build production-grade AI systems.
Provided RLHF annotation resources for model training and alignment. Our team worked on preference labelling to improve model response quality and safety.
Supported model training pipelines with domain-specific annotation and RLHF feedback loops. Contributed to improving model instruction-following across multilingual datasets.
Delivered specialised RLHF resources for enterprise AI system training. Focused on improving model behaviour in structured business and data contexts.
This experience gives us a fundamental understanding of how large language models learn, where they fail, and how to design applications that work reliably in production not just in a demo environment.
Agentic AI means the system can take multi-step actions, make decisions, and complete tasks end-to-end not just answer questions. Here's what we've shipped.
Built for a finance dashboard client an AI agent that reads raw expense data (bank statements, invoices, receipts), automatically maps each transaction to the correct accounting category (salaries, rent, vendor payments, travel, etc.), and generates a structured monthly P&L report ready for the finance team to review.
The agent handles ambiguous transactions by asking a clarification question once and remembering the answer for future similar transactions. Over time it learns the client's specific categorisation preferences. Monthly report generation that previously took a finance analyst 4-5 hours now completes in under 3 minutes.
Built for KIC, an e-commerce client a Swiggy-style conversational support agent that handles the full order support flow without human intervention. Customer types their issue (e.g. "my order is delayed"), the agent presents structured options, guides them through the resolution flow, and where possible completes the action automatically (raising a refund, escalating to delivery partner, placing a reorder).
The agent uses RAG to answer product/policy questions from KIC's knowledge base so "what is your return policy for electronics?" gets an accurate, up-to-date answer pulled from internal documents, not a hallucinated response. Handled 73% of support queries without escalation to a human agent.
Built as part of an HRMS platform two agents that handle the most time-consuming parts of HR operations.
Offer Letter Agent: HR manager enters the candidate name and role. The agent asks 6-8 structured questions (CTC components, joining date, reporting manager, probation period, variable pay structure). It drafts the offer letter, shows it for review, incorporates any changes in natural language ("make the variable 20% instead of 15%"), and sends it to the candidate via email the whole flow takes under 4 minutes.
Onboarding Agent: On day one, triggers a sequence creates accounts across connected systems (email, HRMS, project management tools), sends the welcome email with credentials, schedules week-1 check-in, assigns mandatory training modules, and notifies IT to ship the laptop. Zero manual steps from HR.
We've implemented RAG pipelines for clients across e-commerce, healthcare, and enterprise connecting LLMs to internal documents, product catalogues, and knowledge bases so the AI answers accurately from real data, not hallucinations.
Our RAG implementations use a consistent production-grade architecture: documents are chunked, embedded, and stored in a vector database (Pinecone or pgvector). At query time, the most relevant chunks are retrieved and passed to the LLM as context. We add a confidence threshold below which the system escalates to a human rather than guessing.
PDFs, Word docs, web pages, and structured data automatically chunked, cleaned, and embedded. Supports incremental updates (new documents sync automatically).
Embeddings stored in Pinecone or pgvector. Semantic search retrieves the most relevant chunks for each query not just keyword matching.
Retrieved chunks passed to GPT-4o or Claude as context. The model answers only from what's in the context with source citations.
Below a confidence threshold, the system says "I don't know" or escalates never hallucinates an answer to a business-critical question.
DigiChum Infotech has provided specialised RLHF (Reinforcement Learning from Human Feedback) resources to some of the world's largest AI programmes. Our team has hands-on experience with what it takes to align large language models for production use.
RLHF is the process of teaching language models to produce better outputs by having human annotators rank or rate responses. Our team has worked on:
Beyond RLHF annotation, we fine-tune open-source models (LLaMA, Mistral, Phi) for specific client use cases where a general model isn't sufficient.
Most enterprise AI projects aren't greenfield they're about adding intelligence to existing ERP, CRM, HRMS, and operations systems without disrupting what's already working.
Add AI-powered lead scoring, auto-fill of CRM fields from call transcripts, and intelligent next-action suggestions directly inside Salesforce no context switching for reps.
Natural language queries for ERP data ("show all overdue orders from last month"), AI-powered demand forecasting fed into procurement, and anomaly detection on financial transactions.
Offer generation agent, onboarding automation, AI-assisted JD writing, resume screening with structured scoring, and employee sentiment analysis from survey responses.
Natural language report queries, anomaly detection on lab values (flags results outside expected range for patient profile), AI-assisted differential diagnosis suggestions for doctors.
Business-facing WhatsApp bots connected to your backend order tracking, lead qualification, appointment booking, dealer support. Built on WhatsApp Business API with LLM intent understanding.
Extract structured data from unstructured documents invoices, contracts, lab reports, application forms. Output goes directly into your database or triggers downstream workflows.
Most businesses don't need AI everywhere. They need it in 2-3 places where it will have a measurable impact. Our consulting engagement helps you find those places and build a plan to get there.
We map your current workflows, identify the 3-5 highest-friction manual processes, and assess your data quality and availability. Output: a ranked list of AI opportunities with estimated effort and ROI.
For each opportunity: build vs. buy analysis, data requirements, integration complexity, and risk assessment. We tell you honestly which use cases are worth pursuing and which aren't ready yet.
For the top-priority use case, we build a working POC in 2-3 weeks. Real data, real integration, real output not a demo with mock data. You see whether the AI actually works in your context before committing to a full build.
If the POC validates, we deliver a detailed production roadmap architecture design, data pipeline requirements, integration plan, cost model, and timeline. You can build with us or take it to any team.
30-minute AI readiness call we'll tell you honestly whether AI is the right tool for your current problem, and what the fastest path to value looks like.
Book Free AI Consultation →We've trained models for Apple, Alibaba, and Oracle and built production AI for startups and enterprises across fintech, healthcare, e-commerce, and HR. Let's talk about what you're trying to solve.