Services
We fine-tune large language models for product teams that have outgrown prompt engineering and RAG — priced as lean, fixed-scope tiers, not a six-figure enterprise engagement. SFT, DPO, ORPO, LoRA, and QLoRA on Llama 3.3, Qwen 2.5, Mistral, Phi-4, and OpenAI/Anthropic custom models. Every engagement ships a versioned eval harness, vLLM or TGI inference with INT4/INT8 quantization, and an MLOps loop that catches regressions before users do. An eval PoC from $2,900 in 4–6 weeks, RAG over your knowledge base from $8,100, an autonomous AI agent from $10,400, a fine-tuned model in production from $13,800. Fixed-scope, all-in USD pricing with IP transferred to you on day one.
Senior MLEs who have shipped fine-tuned models to production · your cloud, your weights · GDPR-aligned · ISO 27001 ready · SOC 2 Type II in progress · HIPAA-capable · CET workday with 9 AM–1 PM ET overlap
Most AI teams should not fine-tune. Prompt engineering, structured outputs, and retrieval-augmented generation solve 80 percent of production cases at frontier-API cost. The remaining 20 percent is where fine-tuning earns its keep — behavioural change that no prompt can force, structured-output adherence above 99 percent, latency under 400 ms at p95, or an inference cost curve that breaks at scale. We help teams figure out which side of that line they are on, then ship the smallest model that hits the eval bar. Engagements start with a written feasibility memo and a versioned eval harness; nothing trains until both are agreed in writing.
Golden-set construction, labelling rubrics, inter-annotator agreement tracking, PII redaction with Presidio, synthetic data generation with frontier models, and deduplication. The labelling pipeline lives in your cloud, not ours.
Supervised fine-tuning for behavioural change, DPO and ORPO for preference alignment without a separate reward model, and KTO when preference data is asymmetric. TRL, Unsloth, and Axolotl on your cloud GPUs or ours.
Parameter-efficient fine-tuning cuts GPU memory 60–80 percent and lets you iterate ablations on a single A100. We default to QLoRA 4-bit for first-pass, full fine-tune only when ablations prove the lift is real.
Versioned golden set, LLM-as-judge with rubric scoring (Ragas, custom), task metrics (F1, BLEU, ROUGE, exact match), adversarial probes for hallucination, jailbreak, PII leakage. CI blocks any merge that regresses the bar.
Training-data versioning with DVC or LakeFS, experiment tracking on Weights & Biases or MLflow, scheduled retraining triggered by drift metrics, and rollback runbooks. Everything reproducible, everything in your repo.
vLLM, TGI, or TensorRT-LLM with continuous batching, INT8/INT4 quantization (AWQ, GPTQ, FP8), speculative decoding, and prefix caching. Load-tested at your real p95 traffic before cutover, with monitoring dashboards.
Week 1–2: written memo answering whether fine-tuning is the right tool, which base model fits the task, expected eval lift over RAG/prompt baseline, and total cost projection over 12 months. Go/no-go before any GPU spend.
Weeks 3–5: golden set of 200–1,000 labelled examples, labelling rubric, PII redaction pipeline, eval harness wired to W&B with frontier baselines. Nothing trains until the eval suite runs green against the baseline.
Weeks 6–8: SFT, then DPO or ORPO if preference data exists. LoRA/QLoRA first, ablations on rank, learning rate, and base model. Every run is reproducible from the config file in your repo.
Weeks 9–12: vLLM/TGI deployment, quantization, load tests at p95 traffic, canary rollout with rollback runbook, monitoring dashboards, and engineer handover. Optional MLOps retainer for continuous training.
4–6 weeks. A written feasibility memo plus a versioned eval harness on your real task: base-model recommendation, expected eval lift vs RAG/prompt baseline, GPU cost projection, go/no-go. Credit applied to the next tier if you proceed. From $2,900.
When retrieval beats tuning: RAG grounded in your own corpus with ingestion and chunking, hybrid retrieval and reranking, an eval harness in CI, PII handling and observability. From $8,100.
A multi-step agent that takes actions across your tools, with human-in-the-loop on high-stakes paths, deterministic evals, output filters and a runbook your team owns. From $10,400.
One production model: data curation, SFT plus DPO/ORPO and ablations, vLLM/TGI inference with quantization, MLOps with drift detection, monitoring and engineer handover. From $13,800.
Pricing is all-in and in USD, with IP transferred to you on day one, no recruitment markup and no tool surcharges. GPU compute runs on your own cloud account — you pay AWS/GCP/Azure directly — so you keep the cost lever.
What drives the price: base-model size and whether it is a closed API or self-hosted open weight; how much labelled data exists versus needs curating; the number of training methods in play (SFT only, or SFT plus DPO/ORPO and ablations); inference SLA and quantization targets; and compliance scope — a HIPAA BAA, EU-only data residency or SOC 2 evidence adds data-handling and audit work on top of the model engineering.
Most vendors keep the number for a sales call. Here are our lean, fixed-scope tiers so you can budget before discovery. Everything is all-in, quoted in USD, with IP transferred to you on day one, no recruitment markup, no tool surcharges and no hidden fees. You see the line-item budget before any code is written and sign off on it. GPU compute runs on your own accounts, so you keep the cost lever.
Eval PoC
from $2,900
4–6 weeks · fixed
A written feasibility memo plus a versioned eval harness on your real task: base-model recommendation, expected lift vs RAG/prompt baseline, GPU cost projection, go/no-go. Credit applied to the next tier.
RAG over your knowledge base
from $8,100
when retrieval beats tuning
RAG grounded in your own corpus with ingestion and chunking, hybrid retrieval and reranking, an eval harness in CI, PII handling and observability.
AI agent (autonomous)
from $10,400
multi-step, tool-using
A multi-step agent that takes actions across your tools, with human-in-the-loop on high-stakes paths, deterministic evals, output filters and a runbook your team owns.
Fine-tuned model in production
from $13,800
notebook to serving
One production fine-tuned model: data curation, SFT plus DPO/ORPO and ablations, vLLM/TGI inference with quantization, MLOps with drift detection and handover.
What moves the number: base-model size and whether it is a closed API or self-hosted open weight; how much labelled data exists versus needs curating; the number of training methods in play (SFT only, or SFT plus DPO/ORPO and ablations); inference SLA and quantization targets; and compliance scope — a HIPAA BAA, EU-only data residency or SOC 2 evidence adds data-handling and audit work on top of the model engineering. GPU compute runs on your own accounts, so you keep the cost lever. Prices are indicative and fixed in a written quote for your specific scope.
Native iOS and Android e-signature clients with a Symfony + React CRM for a cross-border law firm — KYC onboarding and a defensible evidence trail for US & EU matters.
Production social platform — App Store + Google Play, live across the US and EU — with geo Radar, encrypted messaging and a virtual economy.
B2B e-commerce and product configurator for a global polymer manufacturer with multi-region pricing, stock and dealer workflows.
A fine-tuned model is only as safe as its fit with your regulatory and operational reality. We pair fine-tuning with industry-specific compliance across US & EU markets, and pull in our sibling AI, ML & data, generative AI integration and RAG-as-a-service teams when a workload needs them.
Domain-tuned assistants for policy, contract and dispute language, plus structured-output models for risk and compliance workflows — with PII redaction at ingress and PCI DSS-scope data handling.
FinTech AI →HIPAA-capable, GDPR-aligned fine-tuning over clinical records and protocols for intake summarisation and care-ops drafting — trained in your VPC with documented data flows and PII-leakage probes in the eval suite.
HealthTech AI →Models tuned on your own catalogue and support corpus for product enrichment, answer generation and merchandising copilots — served on cheap self-hosted inference with per-request cost caps.
Retail AI →Fine-tuned models for exception-handling summaries, shipment and ETA Q&A and back-office drafting over changing operational state — with EU endpoints for EU data and reproducible training configs in your repo.
Logistics AI →GDPR-aligned · ISO 27001 ready · SOC 2 Type II in progress · HIPAA-capable · CCPA-acknowledged
No training run starts until the eval harness runs green against your frontier baseline. Every release ships with a regression report. If the eval bar slips, the merge is blocked — not negotiated.
The MLEs on your engagement have shipped fine-tuned models to production. They know what LoRA rank to pick, why your DPO loss diverged, and how to debug a vLLM throughput cliff — without a Twitter thread.
Training runs in your VPC, weights stay in your S3/GCS, code lives in your repo. We work via assumed IAM roles. No data ever lands on our laptops, and you own the resulting model on day one.
For regulated workloads we sign HIPAA BAAs, run on HIPAA-eligible regions only, and integrate with your existing data governance — not parallel to it.
A loan decision engine that takes ten times less time to approve does not happen by accident. YuSMP built the scoring pipeline, integration with credit bureaus, and a back-office that our underwriters actually enjoy using. Approval turnaround went from two days to under four hours.
Three signals justify fine-tuning. First, latency or cost: a fine-tuned 7B model on vLLM costs roughly 1/40th of GPT-4o for the same task at p95 latency under 400 ms. Second, behaviour you cannot prompt your way into: domain-specific style, structured output adherence above 99%, or refusal patterns that frontier safety layers will not allow. Third, data leverage: you have 5,000+ high-quality labelled pairs that nobody else has. If the answer is mainly knowledge retrieval, do RAG first. If it is occasional formatting, prompt-engineer first. Fine-tuning is the right call when you need behavioural change at scale.
Both. OpenAI fine-tuning (GPT-4o, GPT-4.1, GPT-4o-mini), Anthropic via Bedrock custom models, Google Gemini tuning, and the full open-source stack: Llama 3.3, Qwen 2.5, Mistral, Phi-4, DeepSeek. The choice is engineering, not ideology. Closed models give you faster delivery and zero infra. Open models give you ownership, cheaper inference at scale, and on-premise deployment when compliance requires it. We run the same eval harness against both paths and present the cost-per-token, latency, and quality trade-off in writing before you commit.
Every engagement ships with a versioned eval suite: a golden set of 200 to 1,000 labelled examples curated with the client, automated LLM-as-judge with rubric scoring (Ragas, custom rubrics), task-specific metrics (BLEU, ROUGE, exact match, F1, structured-output adherence), and adversarial probes for hallucination, jailbreak, and PII leakage. Every training run posts to Weights & Biases with the full eval table. CI blocks any merge that regresses the golden set by more than the agreed threshold (typically 2%). The eval suite is yours, version-controlled in your repo, and runs against frontier baselines on every release.
Parameter-efficient methods first: LoRA and QLoRA cut GPU memory by 60 to 80 percent and let us run a Llama 3.3 70B SFT on a single A100 80GB node for under $350 in GPU time. Unsloth and Axolotl give us 2x training throughput vs vanilla Hugging Face TRL. We default to QLoRA 4-bit for first-pass experimentation, switch to full fine-tune only when ablations prove it moves the eval needle. Inference cost is controlled by INT8/INT4 quantization (AWQ, GPTQ), vLLM continuous batching, and speculative decoding. A typical client moves from roughly $20,000/month frontier API spend to about $3,900/month self-hosted inference. GPU compute runs on your own accounts, so you keep the cost lever.
Engagement starts with a mutual NDA and a GDPR-aligned DPA. Training data lives in your cloud account: we work via assumed IAM roles, never copy data to laptops, and the training cluster runs in your VPC (AWS SageMaker, GCP Vertex, Azure ML, or your Kubernetes). For regulated data we sign HIPAA BAAs and run on HIPAA-eligible regions only. PII redaction pipelines (Presidio, custom regex + NER) are part of the data curation step. We are GDPR-aligned, ISO 27001 ready, SOC 2 Type II in progress, HIPAA-capable, and CCPA-acknowledged.
Engagements are fixed-scope and lean, all-in and quoted in USD. An eval PoC runs from $2,900 (4–6 weeks); RAG over your knowledge base from $8,100 when retrieval beats tuning; an autonomous AI agent from $10,400; a fine-tuned model in production from $13,800. The exact number depends on base-model size and whether it is a closed API or self-hosted open weight, how much labelled data exists versus needs curating, the number of training methods in play, inference SLA and quantization targets, and compliance scope. You see the line-item budget at the end of discovery and sign off before any code is written. There is no recruitment markup and no tool surcharges, and GPU compute runs on your own accounts, so you keep the cost lever.
Practical guides on LLM fine-tuning, RAG, and AI model selection for product teams.
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