Capability
AI
We build AI that enterprises can trust in production: grounded retrieval, voice and chat agents, copilots inside tools people already use, and document pipelines that replace manual reading.
Summary
Production AI systems — RAG, voice agents, copilots, document intelligence, and LLM apps that ship into real workflows.
Problem
What teams are stuck with
Most AI pilots stall because they are demos glued to a chat box — no retrieval trust, no evaluation, no ownership in the operating model.
Enterprises need systems that cite sources, respect permissions, and survive messy PDFs, tickets, and call transcripts — not another unbounded chatbot.
Offerings
What we deliver
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01
RAG and knowledge assistants
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02
Voice agents and IVR modernization
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03
Internal copilots and LLM apps
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04
Document AI and extraction
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05
Fine-tuning, evals, and guardrails
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06
Multimodal understanding
Approach
How we work it
We start from the job-to-be-done and the data graph, then choose the thinnest model stack that can be evaluated.
Grounding, tool use, and human escalation are first-class — not afterthoughts bolted on after a demo.
Every engagement includes an evaluation harness so quality is measured before scale.
Outcomes
What good looks like
- AI features that cite, escalate, and stay on-policy
- Measurable answer quality and latency budgets
- Reusable retrieval and agent platforms across products
Sub-services
Inside AI
- RAG & Knowledge Systems Grounded retrieval over docs, tickets, wikis, and product data — with citations, ACL awareness, and eval loops.
- Voice Agents & Conversational AI Telephony and in-app voice agents that book, qualify, resolve, and hand off — with latency and escalation designed in.
- LLM Apps & Copilots Embedded copilots inside dashboards, CRMs, and operator tools — draft, suggest, and act with guardrails.
- Document AI & Intelligent Extraction Parse invoices, contracts, forms, and packs into structured records with human review loops.
- Model Fine-tuning & Evaluation Dataset curation, fine-tuning, and evaluation frameworks so model quality is engineered — not hoped for.
- Multimodal AI Vision, audio, and text together — understand screenshots, product images, diagrams, and meeting media.
Related
Keep exploring
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