Generative AI Solutions
LLM-powered products that write, read, reason and create — built securely on your own data.
- Custom LLM apps & AI copilots on your private data (RAG)
- Fine-tuning and evaluation of open-source & frontier models
- Document intelligence: extraction, summarization, Q&A over PDFs/contracts/emails
- Content, code and design generation pipelines with human-in-the-loop review
- Security-first: VPC/on-prem deployment options, PII redaction, audit logs
Generative AI is moving from hype to the core of how modern businesses operate. At CredenceMind we turn large language models into dependable products: grounded in your data, guarded by evaluations, and measured by business KPIs — not demos.
From a board-level strategy to a production-grade assistant embedded in your product, our engineers handle the full lifecycle: data readiness, retrieval design, model selection, fine-tuning, prompt & agent orchestration, evaluation harnesses, and observability.
What we build
We design generative AI systems around real workflows — sales, support, HR, finance, legal and engineering — so the output is a measurable reduction in manual work, not a chatbot nobody uses.
- RAG knowledge assistants over your wikis, PDFs, tickets and CRMs
- Domain copilots for support agents, recruiters, analysts and developers
- Document intelligence: contracts, invoices, claims, KYC files, medical notes
- Automated content generation with brand voice, review and approval flows
- Code assistants and AI-augmented SDLC (review, tests, docs, migration)
- Multimodal AI: vision models for images, video, scanned documents
- Voicebots and call-center AI with transcription and intent routing
How we work — from pilot to production
Every engagement starts with a 2–3 week discovery and a working proof-of-concept on a slice of your real data. We only scale what shows ROI in the pilot.
- AI opportunity assessment & use-case prioritization by ROI
- Data readiness: connectors, chunking, metadata, permissions
- Model selection: OpenAI, Anthropic, Google Gemini, Mistral, Llama
- Evaluation harness with golden datasets before every release
- Guardrails: PII redaction, prompt-injection defense, output validation
- Deployment: cloud VPC, on-prem or hybrid; SSO & role-based access
- Observability: tracing, cost per request, drift and quality dashboards
Enterprise-grade security & compliance
Your data never trains someone else’s model. We support zero-retention API configurations, private deployments, encryption in transit and at rest, and compliance-aligned processes (DPDP Act, GDPR-ready practices, SOC 2-aligned engineering).
Frequently asked questions
We use zero-data-retention API tiers or private model deployments (VPC / on-prem), enforce PII redaction before any model call, sign DPAs, and never use your data to train models. Access is role-based with full audit logs.
Most business use cases need RAG (retrieval over your documents) because it is accurate, updatable and cheaper. Fine-tuning is for teaching style, format or deep domain behavior. We usually start with RAG and add fine-tuning only when evaluations show it is needed.
A focused pilot takes 2–3 weeks. A production-grade system with integrations, guardrails and evaluation typically takes 8–12 weeks depending on scope.
Yes. We build connectors and agents that plug into your existing tools — CRM, ERP, HRMS, ticketing, chat and document stores — via APIs and webhooks.
We define KPIs up front — hours saved, deflection rate, accuracy vs. golden set, cost per task, CSAT — and ship dashboards so you can see ROI continuously.
Related services
Ready to start with Generative AI?
Book a free 30-minute consultation — we will map your use case and propose a pilot with a clear ROI estimate.