AI Product Development
LLM engineering, agents, computer vision, Indian-language speech, automation, and managed / private / on-premise / edge deployment. Built for Indian languages, accents, TRAI-aware voice, and enterprise control. Research is in the loop: AI Lab, Indian-language AI, agents, vision, speech, and edge.
Intro & problem space
Generic chat is cheap and mostly useless. The hard problems are the ones Geek Theory already writes down on its capability pages: the model must see your documents, the agent must take auditable actions in CRM and ops, the voice agent must hear Hindi and regional accents at 200–350ms, the camera must inspect a line — not a demo photo — and the weights cannot leave the building if legal says so.
India adds constraints the imported stack ignores: languages, Hinglish, DLT/TRAI for outbound voice, cost of inference, and the fact that a lot of the valuable data is still in PDFs, shop-floor video, and call recordings. Geek Theory’s direction is explicit: Building AI for India. Engineering technology for the world. We engineer the full path — data → model → agent → application → infrastructure — and we will tell you when a private GPU box is required and when managed APIs are enough.
Capabilities
foundation models vs fine-tuning vs RAG vs local inference, chosen against the use case. Knowledge systems grounded in company data, evaluation, guardrails, cost/latency-tuned serving, and Indian-language plus business-terminology focus where the corpus needs it.
planner → reasoning → tools → action → verification. Sales, HR, support, operations, research, and marketing agents. Tool control, audit logs, human approval, and connections into real business systems — from AI that answers to AI that works.
OCR, document intelligence, detection, classification, visual inspection, and image/video pipelines. Manufacturing quality, healthcare documents, retail, logistics, and finance. Edge vision when the camera cannot round-trip to the cloud.
speech-to-text, text-to-speech, voice agents, and call intelligence. Accent-aware, multilingual, built for how India actually talks. Support, sales, education, and healthcare voice. Streaming STT (Deepgram / Sarvam AI), RAG-enhanced LLM (GPT-4o / Llama 3 + vector CRM), TTS (ElevenLabs / Sarvam.ai) with Indian accent, Hinglish, and regional language coverage. End-to-end voice latency in the 200–350ms class; interruption detection under 50ms.
TRAI and DLT-aware voice agents, bulk calling with dedicated SIP trunks (Exotel, Tata Tele, Plivo, Ozonetel, direct SIP), predictive pacing, 10,000+ concurrent-call class architecture, human-in-the-loop escalation on sentiment, and live CRM personalization on every call. Not a robocall farm — a compliant voice stack.
data → AI → decision → action → verification. Lead qualification, customer support, document processing, reporting, CRM updates, sales outreach, internal workflows. Lower manual load; keep a verification step.
internal copilots, knowledge, agents, workflow automation, governance, and deployment architecture that fits how the enterprise already works.
cloud-hosted AI infrastructure operated by Geek Theory when you want the capability without owning GPUs.
dedicated environments, private cloud / dedicated servers, air-gapped where required. Private data layer → LLM → agents → applications. Data stays under organisational control.
infrastructure assessment, GPU planning, local inference, networking, monitoring, maintenance. AI that operations can actually run.
small models, quantization, on-device vision and speech, offline and low-latency inference next to the machine or the user.
model serving, API gateways, vector databases, storage, GPU, monitoring, scale. Gemini API vs Vertex AI cost, TCO, security, and unified SDK decisions when the Google stack is in play.
ETL/ELT, warehouses, AI-ready pipelines, analytics, and knowledge bases. Trusted data or the model will hallucinate with confidence.
access, isolation, auditability, model governance, deployment controls. We only claim compliance we can substantiate.
Healthcare
document intelligence, patient workflows, appointment systems, internal knowledge, private AI.
Education
learning systems, platforms, voice AI, knowledge and assessment.
Finance
document AI, internal AI, workflow automation, private AI.
Insurance
claims documents, assistants, support automation.
Manufacturing
vision, predictive systems, quality inspection, operations and edge AI.
Real estate
lead management, property data, assistants, CRM, documents.
Consulting
lead intelligence, research automation, knowledge, CRM, agents.
Logistics
routing, tracking, customer communication, operations AI.
Startups & SMEs
AI MVPs, agents, automation, without enterprise theatre.
device telemetry into models and agents; shop-floor and field intelligence.
structured extraction pipelines that feed RAG and ops, not one-off scrapes.
AI Lab, LLM research (architecture, fine-tuning, quantization, efficient inference), Indian-language AI (speech, text, translation, regional terminology, dataset methodology), agent research (planning, tools, memory, verification), vision research, speech research, edge research. Honest reporting. No unverified benchmark theatre.
Delivery
01
The business action that must change, data that exists (and that does not), latency/privacy/language constraints, and success metrics that are not “we have a chatbot.”
02
Model strategy, RAG vs fine-tune vs local, agent tool surface, speech/vision pipelines, and deployment: managed, private, on-premise, or edge.
03
A working slice on real documents, calls, or images. Kill the approach here if evaluation fails.
04
Production software around the model: apps, APIs, CRM/POS/SaaS connectors, evaluation harness, logging.
05
Chosen environment, GPU or API capacity, TRAI/DLT or sector controls, monitoring.
06
Harden what works, expand tools and languages, keep humans on the exception path.
Internal sequence we run for enterprise: Strategy → Architecture → Pilot → Production → Integration → Scale.
Stack
| Models & serving | Foundation LLMs, GPT-4o-class APIs, Llama 3, RAG, fine-tuning, quantization, local/on-prem inference, Gemini API and Vertex AI when the Google path is the right TCO/security trade. |
|---|---|
| Speech | Deepgram, Sarvam AI, ElevenLabs, streaming STT/TTS, SIP trunks (Exotel, Tata Tele, Plivo, Ozonetel, direct SIP). |
| Agents & orchestration | Tool-using agents, vector databases, CRM-grounded RAG, audit logs, human approval gates. |
| Vision | OCR, document AI, detection/classification, edge cameras. |
| Data & backend | Python, Node.js, ETL/ELT, warehouses, vector DBs, existing Laravel/PHP/MEAN/React estates. |
| Infrastructure | GPU planning, Kubernetes where required, API gateways, private cloud, on-prem, edge devices, CI/CD, monitoring. |
| Mobile/edge clients | Flutter, Android, iOS, on-device small models. |
React JS 
TypeScript 
NODE JS 
Laravel 
MongoDB AI is the direction; engineering is the foundation. We will not sell you a model in isolation. The same company that already runs POS, IMS, CRM, voice, and mobile can put an agent inside those systems with logs and a rollback. India-first is not a slogan on this page: streaming Hindi/regional voice, TRAI-aware outbound, private and edge deployment, and a research lab tied to what we ship. Founded 2019, Chennai. Leadership: Sunthar G S (CEO), Bhupendra Kashyap (COO), Dheeraj Parmar (CTO). Presence in Mumbai and Ujjain. We say what is in production and what is a research direction.
Next step
Describe the workflow, the data, and where the AI is allowed to live (managed cloud, private, on-prem, or edge). We will return architecture, model choices, and phases — not a generic automation brochure.
Discuss an AI Project · Build With Us · info@geektheo.com · +91 90434 86056
Websites that sell, operate, and scale — not brochures that expire in six months.
Native, Flutter, and hybrid apps that survive the store, the network, and the second year.
Multi-tenant products with billing, roles, and the operations logic your category actually runs on.