Fine-Tuning · RAG · Inference · MLOps

Models trained on
your data,
built to stay accurate

Generic models give generic results. We fine-tune, build, and deploy machine learning systems calibrated to your specific domain - with the evaluation rigour and production infrastructure to keep them performing at scale.

Start the Conversation See Live Results
180+ Models in Production
96% Fine-Tune Accuracy
6wk Avg. to Production
4.1× Inference Cost Reduction
99.6% Uptime SLA
What We Deliver

The full ML & LLM engineering stack

From dataset curation and model selection through to inference infrastructure and ongoing retraining - we own every layer.

Discuss Your Model

Fine-tuning a foundation model on your domain data changes everything - the difference between a model that sounds plausible and one that genuinely knows your products, workflows, and terminology. We manage the full pipeline: dataset curation, quality filtering, training runs, and rigorous evaluation against held-out test sets before any production rollout.

Supervised Fine-TuningLoRA / QLoRADPORLHFPEFTHugging Face
96% avg. task accuracy on domain-specific benchmarks

A RAG system is only as good as its retrieval layer - and most failures come from poor chunking, weak embedding choices, or no re-ranking step. We design retrieval pipelines from the ground up: document parsing, chunking strategy, embedding selection, hybrid lexical+semantic search, cross-encoder re-ranking, and full RAGAS evaluation. The result is a system that cites accurately rather than hallucinating.

Hybrid SearchRe-RankingChunk OptimisationWeaviatePineconeLangChain
91% retrieval precision at top-3 across enterprise document sets

Running a large language model at scale is expensive by default. We cut inference costs by 3–5× through a combination of quantisation (GPTQ, AWQ), model distillation to smaller specialised models, continuous batching, and KV-cache management. For latency-critical applications we achieve sub-40ms P95 response times without sacrificing output quality.

QuantisationvLLMDistillationBatchingTritonTensorRT
4.1× average cost reduction at equivalent output quality

Not every problem needs a large language model. Structured tabular data - classification, regression, ranking, anomaly detection - is often better served by gradient-boosted trees with careful feature engineering. We apply the right tool, build explainability into every model for regulatory requirements, and document the decision logic your team needs to maintain it long-term.

XGBoostLightGBMscikit-learnSHAPOptunaFeature Engineering
89% avg. F1 on classification tasks vs 71% baseline

A model that performed well at launch will degrade without the right infrastructure around it. We build the CI/CD pipelines, model registries, feature stores, and drift detection systems that keep your ML investment performing over months and years - not just during the initial validation period.

MLflowKubeflowEvidently AIFeature StoreCI/CDRetraining Triggers
99.6% production uptime across all managed model deployments

Shipping an LLM without a proper evaluation framework is building on quicksand. We design task-specific benchmarks, automated regression suites, and human evaluation protocols before development starts - so every sprint produces a measurable signal, not just a qualitative impression. For production systems, we include red-teaming and prompt injection hardening as standard.

RAGASDeepEvalGiskardGuardrails AIRed-TeamingLangfuse
100% of production LLMs ship with automated evaluation suites
Choosing the Right Approach

Fine-tuning, RAG, or prompt engineering - what your use case actually needs

The three approaches solve different problems. Getting this choice wrong wastes months and significant budget. We make the call based on your data, latency, and accuracy requirements - not trend.

Get a Recommendation
Consideration Prompt Engineering RAG Pipeline ★ Fine-Tuning
Best suited for General tasks, rapid prototyping Knowledge retrieval, document Q&A, live data Most Common Tone, style, task-specific reasoning
Data requirements None Document corpus (any size) 500–50,000 labelled examples
Accuracy on domain tasks Moderate High Very High
Handles knowledge updates ✕ Stale ✓ Real-time ✕ Requires retraining
Source citation / traceability ✕ ✓ Native ✕
Infrastructure cost Low Medium High (training)
Time to production 1–2 weeks 3–6 weeks 6–12 weeks
Hallucination risk High Low (grounded) Low–Medium
Sequere's recommendation Proof-of-concept only Most enterprise deployments Recommended When task consistency matters more than knowledge breadth
How We Fine-Tune

Seven steps from raw data to a model you can trust

Most fine-tuning projects fail at dataset quality - not model architecture. Our process front-loads the rigour where it matters most.

Talk to a model engineer
01

Task Definition & Baseline Evaluation

Define the exact task, success criteria, and evaluation metrics before touching any data. Run the base model against your use case to establish a realistic baseline - this tells us how much headroom fine-tuning can actually provide.

Baseline benchmark report
02

Dataset Curation & Quality Filtering

We audit your existing data, identify gaps, and where needed design a labelling workflow. Low-quality or contradictory training examples actively harm model performance - we apply quality filters, deduplication, and format normalisation before any training run.

Curated training dataset + data card
03

Model Selection & Architecture Choice

Choosing the right base model for the task size, inference budget, and fine-tuning data volume. We compare 3–5 candidate models on your specific benchmark rather than defaulting to the largest or most-discussed option.

Model selection rationale document
04

Training & Hyperparameter Optimisation

Supervised fine-tuning with LoRA or full fine-tuning depending on the data volume and target performance. We use Optuna for hyperparameter search and track all runs in MLflow - every experiment is reproducible.

Trained model checkpoint + training logs
05

Evaluation Against Held-Out Test Set

The model is evaluated against a held-out test set it has never seen, using task-specific metrics alongside general capability benchmarks. We check for catastrophic forgetting and adversarial edge cases before any deployment decision.

Evaluation report with failure analysis
06

Alignment & Safety Pass

For customer-facing or regulated applications, we run DPO or RLHF alignment steps, red-teaming for prompt injection, and Guardrails AI integration for output validation. The model should refuse, rephrase, or flag - not comply blindly.

Safety evaluation report
07

Production Deployment & Monitoring

Optimised for inference with quantisation where appropriate, served via vLLM or BentoML, and connected to Langfuse for production trace logging. Retraining triggers are configured from day one - the model improves with use.

Live model endpoint + monitoring dashboard
Performance Results

What fine-tuning actually delivers on real tasks

Scores from production model evaluations - comparing Sequere fine-tuned models against base model and prompt-only baselines on domain-specific benchmarks.

Scores are averages across 12 production fine-tuning projects, evaluated on held-out test sets with task-specific metrics. Individual results vary by domain, data quality, and task complexity.
Document Q&A - Answer Accuracy (%)
Sequere Fine-Tuned
96
Base GPT-4o + RAG
82
Base Model + Prompt
61
Classification - F1 Score (%)
Sequere Fine-Tuned
94
Pre-trained Classifier
74
Zero-Shot LLM
58
Inference Latency Reduction vs. Full Model (%)
After Quantisation
−72%
After Distillation
−58%
vLLM + Batching Only
−34%
How We Engage

Three ways to work with us

From a two-week model audit to a long-term ML engineering partnership - there's a model that fits where you are.

Model Audit

Best for existing models

A focused evaluation of your current model or ML pipeline - identifying accuracy gaps, inference inefficiencies, drift risks, and the specific interventions that will move the needle fastest.

  • Full model evaluation report
  • Drift & failure mode analysis
  • Prioritised improvement roadmap
  • Cost-benefit model for interventions
Get Started
Most Requested

Custom Model Build

Best for new projects

Our flagship engagement - from problem definition and data curation through to production deployment, MLOps infrastructure, monitoring, and 90-day post-launch support.

  • Dataset curation & quality pipeline
  • Model training & evaluation suite
  • Production deployment + monitoring
  • 90-day post-launch support
Get Started

ML Engineering Retainer

Best for ongoing needs

Senior ML engineers embedded in your team - accelerating your roadmap, managing retraining cycles, and keeping your model infrastructure current on a flexible retainer.

  • 2–4 dedicated ML engineers
  • Fortnightly sprint reviews
  • Model retraining & optimisation
  • Flexible 3–18 month terms
Get Started
Technology Stack

Tools chosen for your constraints, not our convenience

We don't default to the most familiar stack. We benchmark and select based on your latency budget, infrastructure, and task type.

Foundation Models
GPT-4o / o3Claude Sonnet / OpusGemini 1.5 ProLlama 3.1 / 3.3Mistral LargeQwen 2.5Command R+Phi-4
Fine-Tuning
Hugging Face PEFTLoRA / QLoRADPO / ORPOAxolotlUnslothTRLWeights & BiasesMLflow
RAG & Retrieval
LangChain / LangGraphLlamaIndexWeaviatePineconeQdrantpgvectorBM25 + Dense HybridCohere Rerank
Inference & Serving
vLLMBentoMLTriton Inference ServerTensorRT-LLMGPTQ / AWQ QuantisationOllamaModalRay Serve
Evaluation & Safety
RAGASDeepEvalGiskardGuardrails AIPromptfooLangfuseBraintrustRed-Teaming Scripts
Classical ML
XGBoost / LightGBMscikit-learnProphetstatsmodelsSHAP / LIMEOptunaH2O AutoMLPyTorch Tabular
MLOps Infrastructure
MLflowKubeflow PipelinesAirflowFeastDelta LakedbtEvidently AISeldon
Cloud Platforms
AWS SageMakerGCP Vertex AIAzure MLKubernetes / HelmRay ClusterLambda Labs GPUModalHugging Face Inference
FAQs

What clients ask before starting an ML project

Straight answers on model selection, data, cost, and what to expect after go-live. Something else on your mind? Just ask.

That depends on three questions: how frequently does your underlying knowledge change, do you need source citations, and how consistent does the output style and format need to be? RAG is the right choice for most enterprise deployments - it handles live knowledge updates, provides source traceability, and works with very little training data. Fine-tuning is the right choice when you need the model to reason in a specific way, adopt a defined tone, or perform a structured task that prompt engineering alone can't reliably solve. We run a two-week discovery sprint that answers this definitively for your specific use case.
Less than most people assume - but quality matters far more than quantity. A focused supervised fine-tune can produce meaningful improvements with 300–500 high-quality instruction-response pairs. For complex reasoning tasks or production systems with regulatory requirements, 5,000–20,000 examples is a more realistic floor. The more important number is quality: a hundred expertly curated examples consistently outperforms ten thousand noisy ones. Our dataset curation process is where we invest the most time, for exactly this reason.
Yes. We can work within your own cloud infrastructure or air-gapped environment - no proprietary data needs to leave your systems. We have experience deploying on AWS private VPCs, on-premises GPU clusters, and Azure Government environments for regulated industries. All engagement work is covered by NDA from the first conversation, and IP assignment agreements transfer all model weights and code to you on project completion.
Inference costs depend on model size, request volume, and hosting approach. A quantised 7B–13B model served on a single A100 GPU via vLLM typically handles 200–400 requests per minute at under $0.002 per request at scale. We include an inference cost model in every project scope so you see the fully-loaded numbers before committing. For high-volume workloads, model distillation to a smaller task-specific model can reduce costs by 3–5× with minimal accuracy loss.
For RAG systems, grounding responses in retrieved source documents reduces hallucination dramatically - the model cites rather than generates. For fine-tuned models, we train explicitly on "I don't know" responses for out-of-distribution inputs, and integrate Guardrails AI output validation at the serving layer. Every production system includes Langfuse trace logging so we can identify and address hallucination patterns post-launch. There's no single fix - it's a combination of architecture, training data quality, and output validation working together.
For a focused RAG pipeline with a defined knowledge base and a single integration, we typically reach production in four to six weeks. End-to-end fine-tuning projects with evaluation and MLOps infrastructure are six to fourteen weeks depending on data readiness and integration complexity. The two-week model audit or discovery sprint gives you a precise timeline and scope before any larger commitment.
Model drift is inevitable as your underlying data and user behaviour evolves. We build retraining triggers into every production deployment - based on drift thresholds from Evidently AI monitoring, not a fixed calendar schedule. When a trigger fires, the retraining pipeline runs automatically using an updated dataset slice, and the new model version goes through the same evaluation gates as the original before promotion to production. You're alerted, not surprised.
Both, and we choose based on your requirements. Commercial APIs (GPT-4o, Claude, Gemini) are excellent for RAG and general tasks - they're fast to deploy and require no infrastructure management. Open-source models (Llama, Mistral, Qwen) are the right choice when data privacy is a hard constraint, when inference costs at scale become prohibitive, or when you want full ownership of the model weights. We regularly combine both: commercial models for development and evaluation, open-source for production serving.
Ready to Build?

Let's talk about your model - and whether fine-tuning is even the right call

Book a free 45-minute call with one of our ML engineers. We'll look at your use case, your data, and your existing setup - and give you an honest view of the best path forward before you spend anything.

Book a Free Call
No commitment required
NDA available on request
Response within 24 hours