LLM Fine-Tuning

Adapt foundation models to your domain data for accurate, on-brand enterprise AI outputs.

LLM Fine-Tuning

A specialized capability within DEQX AI Transformation — domain-specific model adaptation, proprietary data fine-tuning, evaluation, optimization, and governed production deployment.

Domain-Fit
Models adapted to your terminology & workflows
Private Data
Fine-tuned on proprietary corpora under governance
Eval-First
Benchmarks, regression suites & promotion gates
Prod-Ready
Optimized inference with safety & rollout controls

Fine-Tuning Capabilities, Unified.

From domain adaptation and instruction tuning to evaluation harnesses, optimization, and guarded production rollout — one disciplined program for enterprise-grade LLM performance.

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Domain-Specific Model Adaptation

Specialize foundation models on your industry language, products, policies, and decision patterns so outputs sound and behave like your enterprise — not a generic assistant.

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Proprietary Data Fine-Tuning

Curate, clean, and train on confidential corpora with PII controls, lineage tracking, and access boundaries that keep sensitive knowledge inside your perimeter.

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Evaluation, Safety & Production Deployment

Benchmark quality and risk before go-live. Apply guardrails, optimize latency and cost, then promote models through staged environments with rollback and observability.

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What We Deliver

Structured fine-tuning capabilities that map from data preparation to production inference — designed for regulated enterprises and high-stakes AI workloads.

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Domain-Specific Model Adaptation

  • check_circleIndustry and product terminology alignment
  • check_circleTone, brand voice, and decision-style calibration
  • check_circleMulti-task specialization for enterprise use cases
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Proprietary Data Fine-Tuning

  • check_circleSecure curation of internal documents and transcripts
  • check_circlePII redaction, synthetic masking, and lineage
  • check_circleLoRA / QLoRA / full-parameter strategies by risk profile
terminal

Instruction Tuning

  • check_circleTask-specific instruction and preference datasets
  • check_circleSFT, DPO, and RLHF-style alignment pipelines
  • check_circleRole and tool-use behavior for agents and copilots
assessment

Evaluation & Benchmarking

  • check_circleCustom eval sets mirroring production traffic
  • check_circleRegression suites for quality, safety, and drift
  • check_circleHuman-in-the-loop review for high-risk outputs
speed

Model Optimization

  • check_circleQuantization, distillation, and serving profiles
  • check_circleLatency, throughput, and cost trade-off modeling
  • check_circleHardware-aware deployment for GPU, CPU, and edge
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Guardrails & Safet

  • check_circleContent, policy, and PII filter layers
  • check_circleJailbreak and prompt-injection resistance testing
  • check_circleAudit logs and policy versioning for compliance
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RAG + Fine-Tuning Strategies

  • check_circleDecide when to retrieve, fine-tune, or combine both
  • check_circleGrounded generation with citation-ready retrieval
  • check_circleHybrid architectures for accuracy and freshness
cloud_done

Production Model Deployment

  • check_circleVPC, on-prem, and private cloud inference stacks
  • check_circleCanary, blue-green, and staged promotion gates
  • check_circleRuntime monitoring, cost telemetry, and rollback
tuneDomain-Specific Adaptation

Models that speak your enterprise language.

Generic foundation models miss nuance. We adapt base and instruction-tuned models to your domain so answers reflect your products, policies, regulations, and operating reality — with measurable lifts in accuracy and brand consistency.

  • library_books

    Corpus Engineering

    Build high-signal training sets from SOPs, tickets, knowledge bases, and expert annotations — filtered for quality, balance, and compliance.

  • science

    Adaptation Recipes

    Select LoRA, full fine-tune, or continued pre-training based on data volume, compute budget, and risk — not one-size-fits-all defaults.

  • verified_user

    Brand & Policy Alignment

    Calibrate voice, refusal behavior, and decision criteria so outputs stay on-brand and within approved operating boundaries.

terminalInstruction Tuning

Teach models how your teams actually work.

Beyond knowledge: instruct models to follow task formats, tool protocols, and escalation rules used by support, sales, ops, and knowledge workers — the same patterns that power enterprise copilots and agents.

  • schema

    Task Schemas

    Define structured outputs, tool-call conventions, and multi-step procedures that match production workflows.

  • thumb_up

    Preference Alignment

    Use preference data and pairwise comparisons so models prefer helpful, safe, and policy-compliant responses.

  • smart_toy

    Agent-Ready Behavior

    Prepare models for copilots and autonomous agents with grounded tool use, handoffs, and human approval cues.

hubRAG + Fine-Tuning

Retrieve when knowledge changes. Fine-tune when behavior must stick.

We design hybrid strategies: RAG for fresh, citable facts; fine-tuning for style, reasoning patterns, and domain fluency. The result is accurate answers without sacrificing adaptability as your knowledge evolves.

  • account_tree

    Architecture Decisions

    Map each use case to retrieval-only, fine-tune-only, or hybrid — with clear cost, latency, and governance trade-offs.

  • link

    Grounded Generation

    Pair adapted models with ACL-aware retrieval so answers cite source documents and respect access controls.

  • sync

    Drift Control

    Keep retrieval indexes fresh and re-evaluate fine-tuned behavior as products, policies, and corpora change.

assessmentQuality Assurance

Evaluation & Benchmarking

Promote models on evidence — not demos. We build evaluation harnesses that mirror production traffic, safety requirements, and business KPIs before any model reaches users.

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Custom Eval Suites

Domain-specific golden sets for accuracy, faithfulness, format compliance, and task completion rates.

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Regression & Drift Gates

Automated checks on every candidate so quality and safety never silently regress between releases.

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Human Expert Review

SME scoring for high-stakes domains — legal, clinical, financial, and safety-critical workflows.

Optimization, Safety & Production

Enterprise fine-tuning does not end at training. We harden models for latency, cost, safety, and reliable serving in private and regulated environments.

speed

Model Optimization

Quantization, distillation, batching, and caching profiles tuned to your SLOs and hardware.

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Guardrails & Safety

Policy filters, PII protection, and adversarial testing before and after deployment.

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Private Serving

VPC, on-prem, and air-gapped inference with identity, audit, and data residency controls.

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Staged Rollout

Canary and blue-green promotion with telemetry, cost tracking, and instant rollback paths.

Model & Infrastructure Stack

Cloud-agnostic fine-tuning and serving across open models, commercial APIs, and private GPU fleets — wired into the same enterprise systems that power DEQX AI Transformation programs.

psychologyOpen Models
memoryPrivate GPUs
cloudAWS / Azure / GCP
dnsOn-Premise
databaseVector Stores
account_treeMLOps Pipelines
security

Secure Training Pipelines

Isolated training environments with secrets management, dataset ACLs, and reproducible experiment tracking.

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Serving Anywhere

Deploy adapted models beside RAG and agent stacks — including patterns used in Avyaa AI Copilot and Avyaa AI Company OS.

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Works With Your AI & Data Stack

Fine-tune and serve models where your data already lives — public cloud, private VPC, or fully on-premise.

codePyTorch
hubHugging Face
boltvLLM / TGI
cloudAWS SageMaker
cloud_syncAzure ML
cloud_doneGCP Vertex
deployed_codeKubernetes
dnsOn-Premise

How We Deliver Fine-Tuning Programs

A disciplined path from use-case definition to production models — designed to avoid pilot theater and lock in measurable, governable LLM performance.

Phase 01

Discover

Define success metrics, data readiness, risk constraints, and whether RAG, fine-tuning, or hybrid architecture is the right fit.

Phase 02

Prepare & Train

Curate proprietary datasets, apply privacy controls, run adaptation and instruction-tuning experiments with full lineage.

Phase 03

Evaluate & Harden

Benchmark against golden sets, stress safety and adversarial cases, then optimize for latency, cost, and hardware targets.

Phase 04

Deploy & Operate

Promote through staged environments with guardrails, monitoring, and continuous re-evaluation as data and products evolve.

Frequently Asked Questions

Use RAG when facts change often and answers must cite sources. Fine-tune when you need durable domain fluency, brand voice, structured task behavior, or lower latency without large context windows. Most enterprise programs combine both: fine-tuning for behavior, RAG for fresh knowledge.

Ready to fine-tune models for your enterprise?

Let's Talk