LLM development company shipping production large language model apps.

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.

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.

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.

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.

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.

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.

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.

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.

Prompt engineering first, always — it ships in days, costs nothing beyond inference, and solves most tasks. Move to RAG when the task needs facts outside the model's training data or facts that change too fast for a fine-tune cycle to keep up with; the citation requirement RAG enables is often the real reason to reach for it. Fine-tune only when your eval set shows the foundation model consistently misses on domain terminology, structured-output format, or a latency/cost target that prompting and retrieval genuinely cannot close. Most production LLM apps we ship never need a fine-tune.

Neither, in the abstract — it is a benchmark question against your task, not a philosophy. Closed models (Claude, GPT-4o) usually win on raw capability and time-to-ship for general-purpose tasks and are the right default when data residency and fine-tuning control are not hard requirements. Open-weight models (Llama 3, Mistral) win when data never leaving your VPC is non-negotiable, when inference volume is high enough that self-hosting beats API costs at scale, or when a fine-tune needs full weight access. We benchmark your actual task against both categories on your data before recommending either.

It depends entirely on volume and latency requirements, and we run the numbers on your actual traffic before recommending either. Managed APIs (Claude, GPT-4o, Bedrock) have zero infrastructure overhead and are cheaper at low-to-moderate volume. Self-hosted Llama 3 on vLLM becomes cheaper once inference volume is high enough that the GPU instance cost amortises below the equivalent API spend — the crossover shows up clearly in the eval-and-benchmark phase, not as a guess. Compliance requirements can override the pure cost calculation entirely: if third-party inference is ruled out, self-hosting is the only option regardless of the crossover point.

Ten to twelve weeks for a properly curated fine-tune, most of which is dataset work, not training time. Weeks one and two curate and label the instruction dataset from your real data. Weeks three through six run LoRA fine-tuning with iterative eval rounds against a held-out test set. Weeks seven and eight validate against the production eval harness and tune for latency and cost, including distillation to a smaller model where the accuracy holds. The final weeks deploy to vLLM inside your VPC with monitoring and drift detection wired in. Reference deployment: a Llama 3.1 fine-tune that beat the base model by 22% on the eval set and matched GPT-4o accuracy at 61% lower inference cost.