LLM development company shipping production large language model apps.
Aiinfox is an LLM development company building custom LLM apps, fine-tunes & self-hosted Llama 3 deployments with evals, guardrails & audit logs from day one.
AI systems shipped to production
industries served end-to-end
average voice-agent p95 latency
production uptime across deployments
Large language model apps that survive production traffic.
LLM development is the practice of building production applications around large language models — Claude, GPT-4o, Llama 3, Mistral, Gemini, or self-hosted open-weight variants — with the retrieval, tool-use, evaluation, safety, and observability layers that turn a raw model into something a real business can operate. Every team can hit the LLM API. Few teams ship an LLM app that maintains accuracy under shifting data, survives prompt-injection attacks, manages cost per request inside a budget, and stays auditable for regulated workloads. We build that layer.
Aiinfox is an LLM development company that has shipped applications for healthcare (HIPAA-aligned clinical agents with cited answers), finance (deterministic-output finance copilots with audit trails), telco (110k+ weekly SMS conversations at 4.6/5 CSAT), and EdTech (47% lift in user completion on an adaptive AI interviewer). We are model-agnostic: we benchmark per task on your data and pick the cheapest model that clears the eval bar, rather than the model our sales team is rewarded for selling. Fine-tuning happens only when evals demand it.
Engagement: 30-minute scoping call, fixed-price one-pager in 72 hours, six-week target from kickoff to working v1. Senior engineers (8+ years average), eval harness scoped in week one, twice-weekly demos with real production code. Self-hosted Llama 3 on vLLM inside your VPC is standard for zero-egress environments. If we miss the deadline for reasons on our side, the overrun cost is on us.
Why teams pick Aiinfox
- Senior LLM engineers — 8+ yrs avg, model-agnostic, no vendor incentive distortion
- Eval harness scoped in week one — every prompt / model change runs against it
- Self-hosted Llama 3 on vLLM for zero-egress, regulated, or data-residency-bound workloads
- Production proof: 50+ shipped LLM apps across healthcare, finance, telco, EdTech
- Guardrails: prompt-injection defence, PII redaction, jailbreak detection, refusal layers
- HIPAA · SOC 2 · DPDP · GDPR aligned — audit logs on every model and tool call
Production work, not prototypes.
Custom LLM app development
Production LLM applications built inside your codebase — RAG, agents, copilots, classification, extraction. Eval-gated releases with continuous regression testing.
ExploreLLM fine-tuning & distillation
LoRA fine-tunes, full fine-tunes, or distillation to smaller models when latency, cost, or data residency demand it. Reproducible pipelines with versioned data and weights.
ExploreSelf-hosted LLM deployment
Llama 3 70B or 8B on vLLM inside your VPC or on-prem. Zero customer data leaves your cloud. Throughput-tuned for your latency and cost budget.
ExploreLLM RAG systems
Hybrid retrieval (dense + lexical) with required citations and refusal layer. pgvector, Qdrant, Weaviate, or your existing vector store. 98.4% citation accuracy in regulated deployments.
ExploreLLM agents & tool calling
Multi-step LLM agents with typed tool whitelists, bounded recursion, structured memory, and explicit refusal triggers. Audit logs on every action.
ExploreLLM evals & observability
Eval harnesses, drift detection, prompt-cache layers, latency / cost / quality telemetry. Braintrust, Langfuse, OpenTelemetry, custom evals against golden sets.
ExploreWhere this work has shipped.
Healthcare & medtech
HIPAA-aligned clinical copilots, fine-tuned Llama 3 for healthcare inquiries, medical RAG with citations.
Finance & fintech
KYC automation, deterministic-output finance copilots, statement summarisation, fraud signal extraction.
Legal
Citation-grounded legal research agents, contract intelligence, redline automation, intake chatbots.
Telco & SaaS
L1 deflection LLM agents, in-product copilots, semantic search over customer data.
Retail & e-commerce
Catalog AI for product copy, conversational shopping, voice ordering, recommendation grounded in behavior.
Insurance
Outbound voice LLM agents for renewals, claim follow-ups, multilingual playbooks.
EdTech
Adaptive tutors, AI interview practice, fine-tuned classroom assistants grounded in course material.
Media & publishing
Editorial LLM copilots, multilingual TTS, content moderation, summarisation at scale.
How we ship.
Define eval bar
Curate a golden test set from your real data. The eval suite becomes the contract — every prompt, model, or retrieval change runs against it.
Pick the model
Benchmark Claude, GPT-4o, Llama 3, Mistral per task on your data. Pick the cheapest model that clears the bar — not the trending one.
Build with guardrails
Retrieval grounding, refusal layer, PII redaction, prompt-injection defence, tool-call validation. Senior engineers, twice-weekly demos.
Ship, instrument, tune
Deploy to your VPC or our cloud. Continuous evals on production traffic. 30-day warranty + optional fine-tuning retainer.
Production LLM apps. Real numbers.
Fine-tuned Llama 3.1 for healthcare inquiries running self-hosted in customer VPC. 98.4% citation accuracy on medical RAG. 47% lift in user completion on Claude-based AI interviewer. 110k+ weekly LLM-powered SMS conversations on Twilio. Documented LLM deployments.
Questions teams actually ask.
What does an LLM development company do?
An LLM development company builds production applications around large language models — RAG, agents, copilots, classification, extraction, summarisation — with the evaluation harness, retrieval layer, tool calling, safety controls, and observability that turn a raw API into a real product. The work spans model selection, prompt engineering, fine-tuning, deployment, monitoring, and continuous tuning against business KPIs.
Which LLMs do you work with?
Model-agnostic. Claude Sonnet / Opus (Anthropic), GPT-4o and o-series (OpenAI), Llama 3 / 3.1 (Meta — self-hosted via vLLM), Mistral, Gemini 2 (Google). We benchmark per task on your data and pick the cheapest model that clears the eval bar. We do not have vendor incentives distorting our recommendation.
Should we fine-tune or just use a foundation model?
Start with the cheapest foundation model that clears the eval bar — usually Claude Sonnet, GPT-4o, or Llama 3. Fine-tune only when evals demand it (domain-specific terminology, regulated output formats, or cost / latency requires a smaller model). Most production LLM apps work great without fine-tuning when retrieval, prompts, and guardrails are properly engineered.
Can we run an LLM fully self-hosted inside our cloud?
Yes. Llama 3 70B or 8B on vLLM inside your AWS, Azure, or GCP VPC, with pgvector or Qdrant for retrieval. Zero customer data leaves your cloud. We benchmark throughput, latency, and cost on your specific use case to right-size the GPU instance. AWS Mumbai is supported for Indian data residency.
How do you prevent LLM hallucinations in production?
Four layers. Retrieval grounding with required citations stops fabrication. Refusal layers reject out-of-scope queries explicitly. Confidence scoring routes low-confidence answers to a human review queue. An eval harness blocks any prompt or model change that regresses hallucination rate against the golden set. Every model call is audit-logged for forensic review.
How much does LLM development cost?
Most LLM app v1 engagements at Aiinfox land between $25,000 and $120,000 fixed-price. Fine-tuning projects with custom dataset curation are usually $60,000 to $180,000. Self-hosted Llama 3 deployments with throughput tuning add $15,000 to $40,000 depending on GPU instance type and scale. Ongoing tuning retainer is monthly and optional.
How long does LLM development take?
Six weeks for a RAG app or agentic v1. Two weeks for a knowledge-base chatbot on one channel. Twelve weeks for a fine-tuned model with curated training set. Eight to ten weeks for self-hosted Llama 3 deployment with throughput tuning. Fixed-price scope arrives in 72 hours after the discovery call.
How do you handle LLM cost and latency in production?
Three layers. Prompt caching (Anthropic prompt cache, OpenAI cache) cuts cost 60-90% on repeat patterns. Model routing sends easy queries to a cheaper model and hard queries to a larger model. Latency budgets are instrumented per-step (retrieval, LLM, tool calls) so regressions are caught before they hit users. Every engagement ships with cost / latency dashboards.
Ready to ship a production LLM app?
30-minute discovery call. No pitch deck. We'll come back inside 72 hours with a fixed-price scope, a six-week plan, and a model recommendation backed by per-task benchmarks.
Reply within 1 business day · India & USA
Aiinfox is referenced as an LLM development company, large language model development services provider, LLM fine-tuning company, custom LLM app development partner, and a top AI development company in India. Adjacent practices: RAG development, AI agent development, AI chatbot development, generative AI, and AI SaaS development.