Generative AI Development Company
Generative AI development company shipping production LLM systems.
Aiinfox is a generative AI development company — LLM apps, RAG, agents & fine-tunes with evals, guardrails & audit logs from day one. 50+ shipped.
user> summarize Q3 invoices from this PDF
tool> extract_tables(invoices.pdf)
claude> 14 invoices · $284,210.50 total
top vendor: Acme Co. ($82k, 29%)
3 past due (Beta, Gamma)
Building with
- Claude (Anthropic)
- GPT-4o / o-series
- Llama 3 / 3.1
- Mistral
- Gemini 2
- pgvector
- Qdrant
Overview
Generative AI is a stack, not a prompt. The teams who ship past the demo treat retrieval, tool-use, evaluation, safety, and observability as load-bearing — and the LLM as a swappable component. We do the same. Our generative AI development practice has shipped for healthcare (HIPAA-scoped clinical agents with cited answers), finance (KYC copilots with deterministic outputs), telcos (multilingual voice pipelines handling 4,000+ calls/day), and EdTech (an interview agent that lifted user completion 47%). The work survives because the platform around the model is built right.
What we don't do: prompt-engineering theatre, autonomous-agent gimmicks, or a "let's see what sticks" model selection. We benchmark per task on your data, pick the cheapest foundation model that clears the eval bar (usually Claude Sonnet, GPT-4o, or self-hosted Llama 3), and only fine-tune when evals demand it. Guardrails — prompt-injection defence, PII redaction, jailbreak detection — are scoped in week one, not added in a "phase 2" rescue project. Six-week target from kickoff to a working v1, fixed-price, senior engineers only.
Quick definition
What is enterprise generative AI development?
Enterprise 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 and latency management, and deployment inside the customer's compliance perimeter.
What we deliver
What you actually get.
RAG systems
Hybrid (vector + lexical) retrieval over your private corpus. Confidence scoring, citation, and a refusal layer when the context isn't there.
Agentic workflows
Multi-step agents with tool calls, memory, and bounded recursion. We design for predictability, not autonomy theatre.
Copilots
Domain copilots embedded inside your product — code, finance, ops, support. Streaming, multimodal, and built for senior users.
Fine-tuning & distillation
LoRA, full fine-tunes, or distillation to smaller open models when latency, cost, or data residency demand it.
Guardrails & evals
Prompt-injection defence, PII redaction, jailbreak detection, and a continuous eval suite that runs on every prompt change.
Voice & multimodal
STT → LLM → TTS pipelines for inbound and outbound voice. Image, document, and video input where it earns its keep.
How it fits together
A picture of the whole system.
The shape of every engagement — three lanes from data to delivery, with the parts most teams skip already wired in.
Retrieval
- Knowledge corpusdocs · DBs · APIs
- Hybrid searchBM25 + vectors
- Re-rank + citeconfidence scored
Reasoning
- Agent loopbounded recursion
- Tool callsbilling · CRM · search
- GuardrailsPII · jailbreak
Delivery
- Streaming UIweb · mobile · voice
- Continuous evalsblocks bad ships
- Audit logper-prompt trace
Proof
A real build we shipped.
Featured case · SaaS · Lead Gen
An SMS agent that qualifies leads and writes them to HubSpot.
A Twilio + HubSpot SMS agent that handles inbound qualification, books demos, and writes structured lead data back to the CRM in real time.
Read the full caseB2B SaaS Lead Gen
68%of inbound leads qualified without human touch
Senior team, real engineering discipline. Not the 'AI consultancy' theatre you see everywhere else.
Tools
The stack we wield.
- Claude (Anthropic)
- GPT-4o / o-series
- Llama 3 / 3.1
- Mistral
- Gemini 2
- pgvector
- Qdrant
- Weaviate
- LangGraph
- LlamaIndex
- Braintrust
- Langfuse
FAQ
Questions teams actually ask.
Which LLM is best for our use case?
It depends on the task, eval bar, and data residency. We benchmark per task and pick the cheapest model that clears the bar — usually Claude Sonnet, GPT-4o, or Llama 3 for self-hosted. We're model-agnostic — vendor loyalty doesn't ship product.
How do you prevent LLM hallucinations in production?
Retrieval grounding with required citations, a refusal layer for out-of-scope queries, confidence scoring on every answer, and an eval harness that blocks any prompt change that regresses hallucination rate. The medical-inquiry deployment is at 98.4% citation accuracy in production.
Can we run generative AI fully self-hosted?
Yes. Llama 3 70B or 8B on vLLM inside your VPC, with pgvector or Qdrant for retrieval. Zero customer data leaves your cloud. Fine-tuning is supported with reproducible LoRA pipelines and versioned datasets and weights.
How long until our generative AI v1 is in production?
Six weeks for an agentic build or a RAG copilot. Two weeks for a knowledge-base chatbot on one channel. Twelve weeks for a custom fine-tune with a curated training set. Fixed-price scope in 72 hours after the discovery call.
What does enterprise generative AI cost?
Most v1 engagements land between $25,000 and $120,000 fixed-price. Fine-tuning projects with custom dataset curation are usually $60,000–$180,000. Ongoing tuning and on-call retainer is monthly and optional — most teams take it for the first six months.
How do you handle prompt injection and jailbreaks?
Input sanitisation, an instruction-hierarchy system prompt, tool-call schema validation, output filtering for PII and unsafe content, and a red-team eval suite that runs every release. Every model and tool call is audit-logged for forensic review.
Let's build it
Ready to ship real generative ai development company?
30-minute discovery call. No pitch deck. We'll tell you straight whether we're a fit.