LLM Fine-Tuning
Adapt foundation models to your domain data for accurate, on-brand enterprise AI outputs.
A specialized capability within DEQX AI Transformation — domain-specific model adaptation, proprietary data fine-tuning, evaluation, optimization, and governed production deployment.
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.
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.
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.
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.
Explore Integrations arrow_forwardWhat We Deliver
Structured fine-tuning capabilities that map from data preparation to production inference — designed for regulated enterprises and high-stakes AI workloads.
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
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
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
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
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
Guardrails & Safet
- check_circleContent, policy, and PII filter layers
- check_circleJailbreak and prompt-injection resistance testing
- check_circleAudit logs and policy versioning for compliance
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
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
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.
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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.
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.
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.
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Architecture Decisions
Map each use case to retrieval-only, fine-tune-only, or hybrid — with clear cost, latency, and governance trade-offs.
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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.
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.
Custom Eval Suites
Domain-specific golden sets for accuracy, faithfulness, format compliance, and task completion rates.
Regression & Drift Gates
Automated checks on every candidate so quality and safety never silently regress between releases.
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.
Model Optimization
Quantization, distillation, batching, and caching profiles tuned to your SLOs and hardware.
Guardrails & Safety
Policy filters, PII protection, and adversarial testing before and after deployment.
Private Serving
VPC, on-prem, and air-gapped inference with identity, audit, and data residency controls.
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.
Secure Training Pipelines
Isolated training environments with secrets management, dataset ACLs, and reproducible experiment tracking.
Serving Anywhere
Deploy adapted models beside RAG and agent stacks — including patterns used in Avyaa AI Copilot and Avyaa AI Company OS.
Works With Your AI & Data Stack
Fine-tune and serve models where your data already lives — public cloud, private VPC, or fully on-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.
Discover
Define success metrics, data readiness, risk constraints, and whether RAG, fine-tuning, or hybrid architecture is the right fit.
Prepare & Train
Curate proprietary datasets, apply privacy controls, run adaptation and instruction-tuning experiments with full lineage.
Evaluate & Harden
Benchmark against golden sets, stress safety and adversarial cases, then optimize for latency, cost, and hardware targets.
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?
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