What we mean by AI solutions
Most companies do not need "an AI strategy" as much as they need two or three AI systems that clearly save time or make money. Appmetry's AI solutions practice starts with your workflows, finds the steps where a model can read, write, decide or answer better or faster than today, and engineers that into software your teams actually use.
We have shipped AI in demanding production settings, including on-device pose estimation and capture-quality automation for Uplift Labs, where our engineers have been embedded for more than six years.
AI services we deliver
- Generative AI applications: copilots, content and proposal generators, and internal tools built on large language models (LLMs), with prompts, evaluation and guardrails engineered like any other code.
- RAG knowledge assistants: chat over your policies, product manuals, SOPs, tickets and contracts with source citations and permission-aware retrieval.
- Document intelligence: extract and validate data from invoices, purchase orders, KYC documents, delivery challans and contracts, then push it into your ERP.
- Computer vision and on-device AI: image classification, OCR, pose estimation and quality checks that run on mobile devices or at the edge.
- AI agents and automation: multi-step agents that research, update records and trigger workflows. See AI agent development.
- Voice AI: inbound and outbound AI calling agents. See voice AI and calling agents.
- AI analytics: ask questions of your business data in plain language, as we built for Tally users in BizFinAI.
How an AI engagement runs
- Discovery (1 to 2 weeks). We map workflows, size each candidate use case by value and feasibility, and pick the first one with a clear ROI.
- Data and evaluation set. We gather representative examples and define what "correct" looks like before writing prompts or training anything.
- Pilot build (3 to 6 weeks). A working version is integrated with real systems and tested with a small group of users.
- Hardening. Guardrails, fallbacks to humans, monitoring, cost controls and security review.
- Scale and improve. Roll out to more users, add the next use case and keep improving quality against the evaluation set.
Where AI pays off first
| Function | Typical AI use case | What improves |
|---|---|---|
| Sales | Lead qualification on WhatsApp and web chat, proposal drafts | Response time, conversion |
| Customer support | Answer and route tickets with a knowledge assistant | Deflection rate, handling time |
| Finance | Invoice and bill extraction into ERP, anomaly checks | Manual entry hours, errors |
| Operations | Order and dispatch updates, document checks | Turnaround time, visibility |
| HR | Policy assistant, resume screening support | Time to answer, time to hire |
Why companies choose Appmetry for AI
- Engineering depth. Our founders scaled Poshmark's platform to 80M+ users; we bring the same discipline to AI systems: testing, observability and performance.
- Business context. We also implement Odoo ERP and WhatsApp automation, so AI is connected to where the work actually happens.
- Honest scoping. If a use case does not need AI, or the data is not ready, we will say so and suggest what to fix first.
Getting started
Share the process you want to improve and the systems involved. We will come back with candidate use cases, an indicative timeline and how we would measure success. Book a free consultation.