Trusted by 80+ enterprises ★ 4.9

LLM Fine-Tuning & Custom Model Training
Build a Private AI Model That Knows Your Business

For enterprises that need AI to understand their data, terminology, and domain — not just the internet.

21
YEARS BUILDING
350+
AI ENGINEERS
7
VERTICALS SERVED

Trusted across regulated industries

Market Context

The shift to autonomous systems is already underway

3 externally sourced stats. Purpose: validate the problem is real and the market is moving — not to sell XYZ Tech. Buyer reads and thinks "yes this is happening in my industry."

$71B
Enterprise LLM market by 2035
Future Market Insights
36.8%
CAGR of the LLM services market 2025-2034
Polaris Research
1st
Outsourcing fine-tuning is the top enterprise preference
market consensus 2024

THE GAP

Proof Works. Process fails.

Most agentic AI initiatives stall in the same place — moving from working demo to deployed system with real liability.

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What most enterprises are struggling with

  • Generic ChatGPT gives generic answers — it doesn't know your products, processes, or compliance rules
  • Public LLMs hallucinate on domain-specific content — risk in regulated industries is unacceptable
  • Data privacy — sending sensitive business data to third-party AI APIs is a legal and security risk
  • Model scale at scale — API costs compound rapidly when usage grows across the organisation

How A3 Logics approaches it

  • Fine-tuned on your private data — stays on your infrastructure, not a shared model
  • Domain-specific accuracy — tested against your real use cases before deployment
  • Open-source foundation models — Llama 4, Mistral, DeepSeek — reduced cost, full control
  • RAG architecture layer — combines fine-tuned model with proprietary data for maximum accuracy
What we build

Three deployment patterns. Zero ambiguity

3 delivery types. Each with 1 title and 2 lines of plain-language description. Buyer understands exactly what they can ask for — removes "I don't know what to request" friction.

Domain-specific fine-tuning

Task-specific autonomous agents for defined workflows - RPA replacement, document processing, customer triage

RPA Document AI Triage

RAG system architecture

Coordinated agent teams - researcher, executor, validator - working together on complex enterprise processes

LangGraph CrewAI AutoGen

Private model deployment

Connect agents to your ERP, CRM, HRMS - SAP, Salesforce, ServiceNow - without rebuilding your stack

SAP Salesforce ServiceNow
Technology depth

Proven Technology Selected for Reliability

Battle-tested frameworks, model APIs, and infrastructure — selected for reliability, observability, and enterprise compliance.

Llama 4
Mistral
HuggingFace
LangChain + RAG
OpenAI API
Claude API
AWS Bedrock
Pinecone
Case study

Real deployments , real measurable outcomes

Anonymised reference from a mid-market fintech client — what was built, what it replaced, what it returned.

FINTECH · LOAN OPERATIONS

Replaced a 14-person ops desk with a multi-agent underwriting system.

AGENT PIPELINE

Patient data ingestion
Fine-tuned model inference
Compliance validator
EHR output
Read the full case study
78%
Reduction in documentation time per patient encounter
$1.8M
Annual labour cost recaptured within the first year
96.2%
Accuracy on clinical terminology benchmark across 25,000+ records
Frequently asked

Questions, answered.

Everything CTOs and product leaders typically want to clarify before scoping an engagement. Don't see yours? Bring it to the call.

How long does it take to build an agentic AI system?
Single-agent deployments typically ship to production in 6-10 weeks. Multi-agent orchestration with full system integration ranges from 12-20 weeks depending on integration depth, compliance scope, and data readiness. Discovery is always week 1-2.
Do we need to replace our existing systems?
No. Agents sit alongside your ERP, CRM, HRMS, and call them via API. Most engagements start by wrapping existing tools, not replacing them.
How do you handle data security and compliance?
Models can run inside your VPC or on-prem. We support SOC 2, HIPAA, and GDPR controls — audit trails, role-based access, encryption at rest and in transit, no third-party API calls when self-hosted.
What's the minimum viable starting point?
A 2-week discovery sprint produces a working pilot agent on a single workflow, with measurable benchmarks. From there we scope production rollout based on what actually worked.
How do you measure the ROI of an AI agent?
Per workflow: hours saved per case, error rate vs baseline, throughput, and cost-per-decision. We instrument the agent and pipe metrics to your existing observability stack from day one.

Ready to discuss your use case?

30-minute discovery call with a senior solutions architect. No pitch deck, no slides — just a working session on whether agents fit for your problem.

Book a Free 30-Min Discovery Call

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How can we help?

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