AI Solutions
AI that does real work for your business.
We build AI features into practical software — assistants that answer from your documents, tools that summarize and extract, agents that take on repetitive workflows. Below: what we offer, a concept we are developing, a working sample of the idea, and an honest account of where we stand.
Where we are, plainly
CalmDevLabs is a young studio. The AI services below are offerings we are delivering and capabilities we are actively building — not a list of past AI deployments. The interactive demo runs on fixed sample data, and the product concept is labeled as a concept. We will tell you plainly what we have shipped before and what we would be building with you for the first time.
What we offer
AI services
Each engagement is scoped around a concrete business outcome — not "AI" as a buzzword. Status labels tell you whether an offering is ready to scope today or still being built out.
AI-powered web & mobile apps
Offering now
Applications with AI features built in from the start — smart search, recommendations, assisted data entry, and natural-language interfaces inside the apps we already build.
Typical fit: customer or staff apps where a little intelligence removes a lot of friction.
LLM integrations
Offering now
Integrations with Claude and other language models where appropriate — chosen per use case for quality, cost, and data-handling needs, wired into your existing software.
Typical fit: adding language understanding, drafting, or classification to a product you already run.
AI chatbots & support assistants
Offering now
Customer-support assistants that answer from your own help content, escalate to humans with full context, and learn from the conversations they handle.
Typical fit: businesses drowning in repeat questions over chat, WhatsApp, or email.
RAG & document question-answering
Piloting
Retrieval-augmented generation systems that let staff ask questions in plain language and get answers grounded in your documents — policies, manuals, reports — with sources cited.
Typical fit: teams whose knowledge lives in scattered PDFs, wikis, and shared drives.
Workflow automation & AI agents
Piloting
Agents that take on multi-step repetitive work — triaging inboxes, extracting data from documents, drafting follow-ups — with human review built into every consequential step.
Typical fit: operations teams doing the same structured work every day.
AI-enabled ERP & internal tools
In development
AI capabilities inside business systems — including the ERP and HR portal work we do — such as natural-language reporting, anomaly flagging, and assisted data entry.
Typical fit: businesses already running (or planning) an ERP who want it to work harder.
Status labels: Offering now ready to scope · Piloting early engagements while we harden the approach · In development capability being built.
Interactive sample
Document Q&A, illustrated
A tiny taste of the retrieval idea: ask a question about the sample document below. Illustrative demo — sample data only, not connected to a live AI model.
Sample document Fictional sample data
GreenLeaf Organics — Store Policy (sample)
- Returns accepted within 30 days of purchase with a receipt.
- Refunds go to the original payment method within 5 business days.
- Damaged items are replaced free of charge — just send a photo.
- Bulk orders of 10+ items get a 10% discount, applied automatically.
- Support hours: Monday–Friday, 9am–6pm IST.
Illustrative demo — answers come from the fixed sample above. No AI model is running here.
Product concept
AI Business Assistant Early-stage concept
This is an idea we are developing — not a launched product. It has no users, no revenue, and no live integrations yet. We describe it here so potential customers and collaborators can see where we are headed and shape it with us.
The customer problem
Small businesses run on documents nobody can find: policies in PDFs, price lists in spreadsheets, procedures passed along by word of mouth. Staff waste hours hunting for answers, and customers wait while someone "checks and gets back." The knowledge exists — it is just not searchable in plain language.
Intended target customers
- Small retailers, distributors, and service businesses (10–200 staff) with growing document sprawl.
- Professional firms (clinics, agencies, consultancies) answering repeat client questions from internal references.
- Businesses already using (or planning) an ERP or HR portal who want a natural-language layer over it.
Proposed features
- Ask your documents: plain-language questions answered from company files, with the source passage shown.
- Summarize anything: one-click summaries of long documents, meetings notes, and reports.
- Repetitive-workflows, automated: draft follow-up emails, extract structured data from invoices and forms, triage incoming requests.
- Human in the loop: consequential actions always need a person's approval; the assistant proposes, humans decide.
How Claude could power it
Claude's strengths map directly onto the product's core: natural-language understanding for interpreting vague or misspelled questions, long-context document understanding for summarizing and comparing files, and careful instruction-following for extracting structured data (dates, amounts, names) into business systems. We would use Claude as the language engine behind retrieval-grounded answers — every response traceable to a source document, never invented.
Potential integrations
Google Drive and SharePoint for document sources · popular CRMs and helpdesks for support workflows · Tally/Zoho-style accounting and ERP systems for business data · WhatsApp Business and email for the conversational front end. Integrations would be added based on what early customers actually use.
Realistic MVP roadmap
- Phase 1 — Document Q&A pilot. Upload PDFs, ask questions, get sourced answers. One pilot customer, one document set, heavy human oversight. Now
- Phase 2 — Summarization & extraction. Add document summaries and structured data extraction (invoices, forms) behind an approval step.
- Phase 3 — Workflow automation. Connect approved actions to email, CRM, and ERP — always with human review on consequential steps.
- Phase 4 — Hardening. Access controls, audit logs, data-retention policies, and the evaluation harness that keeps answers honest as document sets grow.
Our approach
How we intend to use Claude
When a project calls for a language model, Claude is our default choice for the language layer. Here is what we plan to use it for — and where the line is today:
- Natural-language interfaces — chat and search experiences where users ask in their own words, including vague or imperfect queries.
- Document understanding & summarization — making long documents queryable and skimmable without losing the source of truth.
- Structured information extraction — pulling dates, amounts, names, and line items out of unstructured documents into business systems.
- AI-assisted workflow automation — drafting, triaging, and proposing next steps, with humans approving anything consequential.
Current vs. planned
Today: we prototype with LLM APIs (including Claude) and build the surrounding software — document ingestion, retrieval pipelines, review interfaces, and integrations. Planned: production retrieval systems with evaluation harnesses, access controls, and audit logging for business-critical use.
What we do not claim. CalmDevLabs is not an Anthropic partner, holds no Claude certification, and has not applied for startup credits. Any future program participation will be disclosed if and when it happens.
Have documents nobody can search? Let's talk.
Email us at our company address or send a project brief — we reply personally.
Our other services
AI works best inside solid software. Our core practices — Android apps, web platforms, ERP & HR portal, backend & APIs, and maintenance — are where AI features land.