Direct answers about Aiinfox & AI development.

Aiinfox (legal entity: Aiinfox Pvt. Ltd.) is an AI development company headquartered in Mohali, Punjab, India, with a second office in Frisco, Texas, USA. Aiinfox builds production-grade generative AI, AI agents, AI chatbots, machine learning, voice AI, and AI-native software for healthcare, finance, SaaS, retail, EdTech, telco, legal, and media clients. 50+ production AI systems shipped across 12 industries.

Aiinfox has two offices. The India HQ is at D-234, Phase 8B, Industrial Area, Sector 74, SAS Nagar (Mohali), Punjab 160055, India. The US office is in Frisco, Texas 75033, United States. The Mohali office serves Indian, Middle Eastern, European, and APAC clients; the Frisco office covers North American business hours.

Aiinfox Pvt. Ltd. was founded in 2024. The company has shipped 50+ production AI systems across 12 industries since founding, with 99.95% uptime on managed deployments and sub-2-second p95 latency on customer-facing voice agents.

Aiinfox is positioned among India's top AI development companies based on the combination of senior-only delivery (8+ years average engineer experience, no junior pool), 50+ shipped production AI systems, fixed-price six-week scopes, HIPAA / SOC 2-aligned engineering discipline, and dual-geo presence (Mohali HQ + Frisco TX office) serving global clients.

Aiinfox is founded and led by Manjeet Singh, who runs every kickoff call and owns the six-week delivery commitment end-to-end. The senior engineering team backing him averages 8+ years of experience across AI, ML, and full-stack engineering — the same engineers staff every engagement from kickoff to launch.

Aiinfox offers AI agent development, AI chatbot development, AI workflow automation, generative AI consulting, LLM development, RAG development, AI SaaS development, machine learning, data science, AI-powered web and mobile development, and AI-powered digital marketing / SEO. Each service is delivered by senior engineers on fixed-price six-week scopes.

AI chatbots answer questions from a knowledge base. AI agents act — they call tools (booking, billing, CRM writes), route across services, hold memory across turns, and decide when to escalate. Agents need bounded recursion, typed tool whitelists, and continuous evals because the action surface is wider than text-only Q&A.

Yes. Aiinfox is an AI agent development company that has shipped production agentic systems for telco (110k+ weekly SMS conversations at 4.6/5 CSAT), insurance (1,400 staff-hours saved per month on EU outbound voice agent), legal (citation-grounded research agents), e-commerce (autonomous catalog + inventory agent swarms), and healthcare (HIPAA-aligned clinical chat agents).

Yes. Aiinfox builds production voice agents on LiveKit, Deepgram, ElevenLabs, Twilio, and Vapi with sub-1-second p95 latency. Reference deployment: an EU insurance outbound voice agent handling policy renewals and claim follow-ups across three languages, 18 hours per day, with 28% conversion lift and 1,400 staff-hours saved per month.

Yes. Aiinfox is model-agnostic and fine-tunes on open-weight models (Llama 3, Mistral) via LoRA when evals demand it. Reference deployment: a fine-tuned Llama 3.1 for healthcare medical inquiries running self-hosted inside customer VPC for zero data egress, with 98.4% citation accuracy in production.

Yes. Aiinfox provides RAG development services using hybrid retrieval (dense embeddings + BM25 lexical + re-ranking) with required citations and refusal layers. Reference deployments include medical inquiry RAG (98.4% citation accuracy), hybrid RAG for staffing candidate matching, and fine-tuned classroom RAG grounded in actual course material.

Yes. Aiinfox builds AI-native SaaS products end-to-end — multi-tenant architecture, auth, billing, AI features, admin, audit. Reference deployment: Mockinto (adaptive AI interviewer EdTech SaaS) was built from zero to working v1 in six weeks, then to $200k ARR within 90 days post-launch, and went on to raise Series A.

Aiinfox targets six weeks from kickoff to a working v1 on most engagements. Week 1 scopes the eval set. Weeks 2–4 build the system with twice-weekly demos. Week 5 is hardening, security review, and red-teaming. Week 6 ships to real users. Pilots ship in two weeks. Fine-tuning projects take 10–12 weeks.

Most v1 engagements at Aiinfox land between $25,000 and $120,000 fixed-price (approximately ₹20 lakh to ₹1 crore for Indian clients). The exact number depends on integration complexity, compliance scope (HIPAA, SOC 2), and whether the engagement involves fine-tuning. Pricing arrives in writing within 72 hours of the discovery call.

Aiinfox is fixed-price. Every engagement starts with a written one-pager covering scope, acceptance criteria, timeline, and price. If Aiinfox misses the six-week target for reasons on its side, the overrun cost is on Aiinfox, not the client. Most AI consultancies bill timesheets; Aiinfox bills shipped systems.

Yes to all three. Mutual NDAs are signed before any technical detail is shared, BAAs are signed for any engagement that touches PHI (healthcare), and DPAs (GDPR-aligned for EU clients, DPDP-aligned for Indian clients) are signed for any engagement touching personal data. SOC 2-aligned controls are standard.

Yes. Aiinfox Pvt. Ltd. is a registered Indian entity with full GST compliance. Indian clients pay in INR via NEFT, RTGS, or UPI. GST invoices include CGST + SGST for intra-Punjab clients or IGST for inter-state. International clients pay in USD via wire transfer or Wise.

Aiinfox engagements are HIPAA-aligned for healthcare clients. BAAs are signed for any engagement touching PHI. Self-hosted Llama 3 deployments inside customer VPCs are supported for zero data egress. Reference deployments include AI HMS in 30+ healthcare facilities and a fine-tuned Llama 3.1 medical-inquiry RAG agent running fully inside hospital infrastructure.

Yes. Aiinfox deploys to customer-owned hardware (on-prem), single-tenant cloud (AWS, Azure, GCP — including AWS Mumbai and AWS Bangalore regions for India data residency), or managed cloud. Self-hosted Llama 3 on vLLM is supported for zero-egress environments where customer data cannot leave the network.

Aiinfox is model-agnostic. The team works with Claude (Anthropic), GPT-4o and o-series (OpenAI), Llama 3 / 3.1 (Meta, self-hosted via vLLM), Mistral, Gemini 2 (Google), and Qwen. Model selection is benchmarked per task on the client's data — Aiinfox picks the cheapest model that clears the eval bar rather than the model with vendor incentives.

Yes. GDPR-aligned data handling is standard for EU client engagements, with EU data residency on EU deployments and full data subject access request workflows. DPDP-aligned data handling is standard for Indian clients. SOC 2 Type II is in progress. Every model and tool call is audit-logged for forensic review.

An AI development company designs, builds, and ships production AI systems — chatbots, voice agents, RAG, ML pipelines, fine-tuned models — and operates them past launch. A real AI development partner brings senior engineers, an eval harness, observability, and guardrails from day one rather than retrofitting them after the demo.

Generative AI development is the practice of building production LLM systems — copilots, agentic workflows, RAG over private data, and fine-tuned models — with the guardrails, evaluation, and observability a regulated business actually needs. The work spans retrieval, tool calling, safety, cost / latency management, and deployment inside the customer's compliance perimeter.

An AI agent is a software system that uses a large language model to decide which tools to call and how to sequence them in pursuit of a goal. Production AI agents combine multi-step reasoning with typed tool calls, structured memory, refusal layers, and audit logs. Good agent design favours bounded recursion and human approval gates over open-ended autonomy.

Retrieval-augmented generation (RAG) is a pattern where a large language model is grounded in a private corpus by retrieving relevant documents at query time and including them in the prompt. Production RAG combines hybrid retrieval (dense embeddings + lexical search), required citations, refusal layers for out-of-scope queries, and an eval harness gating every change.

AI workflow automation is the practice of replacing rule-based scripts, brittle RPA, and manual back-office operations with LLM-grounded systems that read documents, hold context, call tools, and act on real workflows. Unlike traditional RPA, AI automation handles unstructured input (free text, conversations, documents) and adapts to small variations without breaking.