Models that,
actually know your domain.
We fine-tune, distill and align LLMs on your data — from LoRA adapters to full pre-training runs. Senior ML engineers, eval-driven delivery, measurable accuracy gains.
4.9/5 · 180+ teams shipping with our engineers · NDA on request
Zia LLM Development & Fine-Tuning animated chat mockup
Zia · LLM Engineer
Online · responds in ~2s
— WHAT WE DO
Six model workstreams, one training team.
From LoRA adapters to full alignment pipelines, we run the fine-tuning approach that fits your data and budget — never the other way round.
Domain Fine-Tuning (LoRA/QLoRA)
Adapt open-source LLMs to your terminology, tone and edge cases at low cost.
Llama
Mistral
Qwen
Instruction & Chat Alignment
SFT + RLHF/DPO pipelines so the model follows your house style and policies.
SFT
DPO
RLHF
Distillation & Small-Model Builds
Compress a large model’s behavior into a smaller, cheaper model for production.
Distillation
Quantization
Edge & on-prem ready
Retrieval-Augmented Fine-Tuning
Combine RAG with fine-tuning so the model retrieves and reasons in your domain’s voice.
RAG
Embeddings
Grounded outputs
- Featured · Fine-Tuning
Training runs that ,
prove the lift, not just log loss.
Multi-step agents that plan, call your tools (CRM, ERP, ticketing, payments), self-correct on failure, and close the loop — with human-in-the-loop guardrails and full traceability.
Data curation
Fine-tuning
Eval harness
Deployment
1
CURATE
Clean & label training data
2
TRAIN
Fine-tune & align the model
3
VALIDATE
Eval against baseline & ship
Outcome
↑ 22pt accuracy lift ↓ 61% inference cost vs closed-model baseline
14 years · 290+ bots
Built to,
production, not demo.
80+
Models fine
across 9 industries
14yrs
Years in business
founded 2012
95%
Engagement extension
clients renew or expand
<2wk
Kickoff time
data audit → first training run
Capabilities & stack
Boring infra that
ships reliable models.
We pick the base model and training method for the task, not the leaderboard. Closed-source where fine-tuning access is limited, open-source where you need full control. Always evaluated, never vendor-locked.
How we pick
01
Baseline eval before training
Every base model benchmarked on your task before we touch a single weight.
02
Cost & compute budgeted upfront
GPU-hours, per-run cost and expected accuracy lift estimated before kickoff.
03
Swap-ready checkpoints
Model-agnostic training pipeline so you can retrain on a new base without a rewrite.
04
Guardrails by default
Toxicity, PII leakage and hallucination checks built into every eval suite.
BASE MODELS
Llama 3.1 / 3.3
Mistral Large
Qwen 2.5 DeepSeek-V3
Phi-4
GPT-4o (fine-tuning API)
Claude (fine-tuning where available)
Gemma 2
TRAINING FRAMEWORKS
Hugging Face TRL
Axolotl
LlamaFactory DeepSpeed
PEFT
Unsloth Ray Train
torchtune
DATA & LABELING
Label Studio
Argilla
Prodigy Snorkel
Scale AI
Custom pipelines
OBSERVABILITY & EVAL
Weights & Biases
MLflow
Langfuse Ragas
TruLens
OpenLLMetry
PromptLayer
SERVING & DEPLOYMENT
vLLM
TGI
Triton Inference Server Bedrock
SageMaker
Modal
RunPod
Industries
Fine-tunes,
tuned for your industry.
Every industry has its own vocabulary, edge cases and data sensitivity. We bring domain-specific training playbooks, not generic base models — and adapt them to your data on day one.
Healthcare
Fine-tuned clinical note summarization models
- HIPAA-compliant training pipelines (on-prem/VPC)
- Symptom
- triage classification fine-tunes
Finance & Fintech
Fine-tuned models for dispute & fraud-flag classification
- Domain-adapted advisor copilots on internal terminology
- Regulatory-aware output filtering
- Advisor in-context copilot
Retail & E-commerce
Fine-tuned product-description & catalog-enrichment models
- Brand-voice aligned support
- marketing copy models
- Sentiment & review classification fine-tunes
Logistics
Fine-tuned document extraction models (BoL, invoices, manifests)
- Exception-classification models for dispatch teams
- On-device distilled models for driver-side apps
- Voice dispatch for drivers
Engagement models
Pick how you want to
Train with us.
Three ways to engage — all senior ML engineers, all eval-driven, all measured against the accuracy and cost targets we agree on before the first training run.
Fixed-scope
Data & Eval Audit
Best for teams unsure if fine tuning is worth it yet.
- Data readiness & quality audit
- Baseline eval on your task
- Fine-tune vs RAG vs prompt-engineering recommendation
- Go/no-go report with cost estimate
Highlighted
Fixed-scope
Fine-Tuning Build
Best for a specific model with a clear training goal.
- Fixed scope, fixed price, fixed timeline
- Data curation → training → eval in 6–10 weeks
- 90-day post-deployment support included
- Eval harness & model card documentation
Embedded
Staff augmentation
Plug in vetted ML engineers into your team.
- 48-hour shortlist of senior candidates
- Time-zone aligned, full-stack or specialist
- Replace or extend at any sprint boundary
- Direct lines to your eng manager
FAQ
The questions
everyone asks before training.
Still curious? Ask Zia in the hero chat — or book a 30-min strategy call with a senior ML engineer.
How long does a fine-tuning engagement take, end to end?
A data & eval audit wraps in 1–2 weeks; a fixed-scope fine-tune with a working checkpoint is typically 6–10 weeks; a full pipeline with continuous retraining and monitoring is 10–14 weeks. We share a fixed training plan + eval milestones after the data audit.
Do you fine-tune closed models (GPT, Claude) or only open-source?
Both. For closed models we use provider fine-tuning APIs (e.g., GPT-4o) where full weight access isn’t needed; for open-source (Llama, Mistral, Qwen) we run full LoRA/QLoRA or full-parameter training when you need on-prem control or lower per-call cost. We recommend based on your data sensitivity and budget, not a default.
How much data do we need to fine-tune a model?
For LoRA-style adapters, a few hundred to a few thousand high-quality labeled examples is often enough to see meaningful lift. Full alignment (SFT/DPO) or domain adaptation typically needs 5,000–50,000+ examples depending on task complexity. We run a data audit first to tell you exactly where you stand.
How do you evaluate accuracy before and after fine-tuning?
We build a custom eval suite and golden dataset from your task before touching the model, benchmark the base model on it, then re-run the same suite post-training. You get a clear before/after accuracy delta, not just a training loss curve.
Can you fine-tune on our internal, sensitive data securely?
Yes — training can run entirely on-prem or in your own VPC, with no data leaving your environment. For regulated data (healthcare, finance) we follow HIPAA/SOC2-aligned pipelines and strip or mask PII before any training run.
What about hallucinations and overfitting?
Every fine-tune goes through held-out validation, hallucination checks, and regression testing against the base model’s general capabilities — so we catch overfitting and capability loss before deployment, not after.
Will the fine-tuned model work alongside our existing RAG/chatbot?
Yes — fine-tuning and RAG aren’t either/or. We commonly fine-tune a model for tone/reasoning style and pair it with your existing retrieval layer, so the model speaks your domain’s language while still pulling live, grounded answers.
What does it cost?
Cost depends on base model, data volume, and training method — LoRA fine-tunes are significantly cheaper than full pre-training. We share exact compute and engineering cost estimates after the data & eval audit, with no surprise billing.
Healthcare · HIPAA-ready since 2012
Let's fine-tune a model worth deploying.
Send your brief — a senior ML engineer (not a sales rep) replies within 24 hours with a scoped training plan, base-model recommendation and a fixed timeline.
- A 30-min discovery call with a senior ML engineer
- Fixed-scope proposal with timeline & milestones
- Base-model & training-method recommendation for your data
- Eval suite design (yours to keep)
- Two case studies from your industry
