Services / AI Engineering
We adapt models where the evidence supports it, and deploy them with the controls production needs. From a strong baseline and curated data to evaluated artefacts, secure serving and a clear operating plan.
Task-specific evidence. Reproducible experiments. Controlled releases.
The business case
Fine-tuning can improve selected behaviour, consistency and task performance. It is not a default requirement for an AI application, and it does not turn a model into a reliable store of changing facts. We first establish what is failing and whether adaptation is the simplest useful intervention.
A recurring task with measurable errors, representative authorised examples and enough usage or operational value to justify the work. A smaller model or private serving requirement can also be assessed against a clear quality bar.
Missing context, weak retrieval, inconsistent instructions, unvalidated actions or broken integrations. Training cannot repair an unreliable system contract, and extra model complexity should not hide a simpler engineering problem.
Adaptation choices
We compare alternatives against your task, constraints and baseline. These approaches are not a ladder every project has to climb, and deployment does not always require training.
Start with explicit instructions, examples and output validation. Often the fastest baseline for behaviour and formatting problems.
Use when the model can already perform the task and consistency improves with a clearer contract.
Supply current, authorised knowledge or query systems at runtime. Keep facts traceable and access-controlled.
Use when the gap is missing or changing knowledge. Fine-tuning is not a replacement for up-to-date sources.
Learn task behaviour from curated input-output examples on a supported model. Compare adapter-based and managed options.
Use when the baseline has repeatable behaviour errors and representative examples are available.
Use reviewed preferred and rejected responses to refine selected behaviour, where the model and training setup support it.
Use only with reliable preference data and a task-specific evaluation. Preference alignment does not establish factual accuracy.
Assess domain-text adaptation or transferring behaviour into a smaller model when simpler approaches are insufficient.
These can add material data, compute and evaluation work. Check data rights, teacher-model terms and the risk of lost capabilities.
Serve an existing model with the right capacity, access controls and integration contract.
Use when the model already meets quality needs and the real requirement is hosting, privacy or operational control.
Example engagements
These are illustrative scopes, not customer performance claims. Feasibility depends on model support, data quality, licences and the actual production workload.
Adapt a model to your task's format, terminology and response conventions when prompting alone leaves persistent errors.
Validate the result in application code. Fine-tuning does not guarantee schema compliance or factual correctness.
Measure: Schema validity, field correctness and correction effort.
Train against labelled examples for document routing, intent classification or specialised tagging.
Compare with smaller classifiers first. Keep uncertain and high-impact cases on a defined review path.
Measure: Per-class precision and recall, coverage and error impact.
Improve adherence to an agreed tone, answer structure and support workflow using curated examples.
Keep changing policies and customer facts in approved retrieval or systems, not only in model weights.
Measure: Instruction adherence, supported answers and human review quality.
Adapt a suitable text or multimodal model for recurring semantic fields and document patterns.
Benchmark unseen templates and source quality. Extraction still needs evidence, validation and exception handling.
Measure: Critical-field accuracy, missed values and reviewer load.
Assess whether a smaller adapted model can meet the quality requirements of a narrow production workload.
Include training, GPU capacity and operating effort in the comparison. Lower parameter count is not proof of lower total cost.
Measure: Quality at required throughput, latency and total cost.
Deploy a licensed model in an agreed environment with controlled access and application integrations, with or without fine-tuning.
Validate all dependencies, telemetry and data paths. Private hosting is not a compliance or security guarantee.
Measure: Quality, capacity, permission integrity and operational reliability.
From experiment to endpoint
A versioned dataset adapts an agreed base model. We compare the candidate with the baseline, validate the deployment artefact and promote it through a controlled serving path. The evaluated model and the running model must remain traceable to each other.
Freeze a model revision, task contract and evaluation set. Measure what prompting, retrieval or an existing specialist model can achieve before spending on training.
Run an agreed adaptation method with versioned data and configuration. Store checkpoints and experiment evidence under appropriate access controls.
Verify the base model and adapter pairing, or merged weights where appropriate. Check tokenizer and chat-template compatibility, quantisation effects and target runtime behaviour.
Release to a bounded pilot or staged traffic, monitor quality and reliability, and preserve the previous accepted version for rollback. Application-level safeguards remain in force.
Data engineering
Training amplifies patterns in your examples, including mistakes and bias. We establish an authorised, versioned dataset and a defensible evaluation split before choosing training parameters.
Confirm that the data may be used for the intended training and deployment. Minimise personal data, remove secrets and review source licences, retention and any provider processing terms.
Cover the actual task distribution, edge cases, languages and refusal boundaries. Define annotation guidance and review inconsistent labels rather than just collecting more rows.
Deduplicate across training, validation and test sets. Separate related records, customers or time periods where appropriate so near-identical examples do not inflate results.
Use generated examples only with permission, filtering and quality checks. Synthetic volume cannot substitute for real-world coverage; preserve held-out representative evaluation data.
Training engineering
We compare training candidates with the original model and a strong prompting baseline. The method depends on model support, task complexity, memory, licensing and how the result will be served.
Train selected adapter parameters instead of all weights when appropriate. Verify target modules, adapter compatibility and serving support; smaller training artefacts still require the base model.
Evaluate quantised-base adapter training where supported. It can reduce training memory needs, but compute, quality and serving behaviour must be measured on the chosen model and hardware.
Consider updating all weights only when the task and evidence justify the added compute and operational complexity. Track overfitting and regression in capabilities outside the narrow training task.
Record base-model revision, data version, configuration, seeds, checkpoints and evaluation results. Control experiment budgets and promote only a reproducible, reviewed candidate.
Evaluation & release gates
Training loss is a diagnostic, not business acceptance. We evaluate candidates on held-out task examples, safety boundaries and the deployed serving configuration, with release criteria agreed before selection.
Measure task-specific correctness, format adherence, supported answers and error severity. Compare against the untuned model and simpler alternatives; retain cases where the new model gets worse.
Test prompt injection, unauthorised disclosure, memorisation probes and inappropriate tool requests. Access permissions and action validation remain outside the model.
Use a documented rubric and qualified reviewers for subjective or specialist tasks. Track disagreement and blind comparisons where feasible; model-as-judge scores need calibration, not unquestioned trust.
Re-evaluate quantised or merged artefacts on the target runtime. Test realistic context lengths, concurrent requests and failures rather than assuming training-time quality carries over unchanged.
Deployment architecture
We design around your workload and operating constraints. Managed endpoints, dedicated capacity and self-hosted serving offer different control, cost and maintenance tradeoffs; no option is universally best.
Compare supported model families, regional processing, provider terms, capacity and operational ownership. A managed fine-tuned model may not be exportable; decide portability needs before training.
Check model architecture, GPU memory, context lengths, KV-cache demand, batching and concurrency. Confirm driver, kernel and quantisation support on the actual target hardware.
Benchmark supported precision, quantisation and runtime optimisations against quality and workload. Weight size alone does not determine latency or total serving memory.
Authenticate callers, scope access, validate input and output, and enforce rate limits. Keep service credentials, business rules and tool permissions outside model-generated content.
Use versioned artefacts, staged traffic, compatible application contracts and a tested rollback. Adapter or model changes are releases, not invisible replacements.
Bound request queues, output length and timeouts. Test cold starts, out-of-memory events, unavailable providers and overload; fallback models must meet the workflow's own acceptance criteria.
Production operations
Deployment needs an operating plan for both software and model behaviour. We agree responsibilities, privacy-aware evidence and a change process rather than promising a model that never needs attention.
Review sampled outcomes and task signals within agreed privacy limits. Track changes in inputs and errors; retraining follows evidence and approval, not an automatic calendar.
Monitor queueing, latency, errors, throughput and spend. Define capacity and service targets after representative measurements, including peaks and hardware availability.
Protect checkpoints, adapters and datasets as sensitive assets. Maintain an inventory of versions, licences and dependencies, with least-privilege access and retention rules.
Document pausing, routing changes, rollback and investigation. Review model, prompt, data and runtime upgrades together because each can alter production behaviour.
Technology stack
These are options we can evaluate, not a mandatory bundle or a claim that every engagement uses them all. Selection depends on model support, licensing, regional availability, hardware compatibility and your team's operating capacity.
Evaluate available model families and provider-supported tuning options. Availability, regions, supported tasks, export rights and pricing are checked for the specific model at project start.
Build supervised or preference-based experiments using supported model architectures. Select a reproducible toolchain rather than using every framework together.
Version datasets, configurations and checkpoints. Assess where experiment metadata and samples are stored; hosted tracking must meet your data-handling requirements.
Assess distributed training, memory optimisation and quantisation where compatible. Hardware and kernel support, quality impact and total compute cost are validated, not assumed.
Choose a supported serving approach for model architecture, adapters, hardware and workload. Benchmark batching, context limits and operational complexity on target infrastructure.
Integrate model endpoints with scoped APIs, deployment controls and capacity management. Kubernetes is optional, not a prerequisite for every workload.
Combine custom task tests, relevant general benchmarks and service telemetry. General benchmark scores do not replace evaluation on your workflow; redact sensitive trace content.
Open weights do not automatically mean unrestricted use. Base-model and dataset licences, derivative artefact terms and managed-provider restrictions need review for the intended use.
Economics & delivery
Assess data preparation, annotation, experiments, evaluation, storage, serving and maintenance alongside quality. Capacity utilisation and idle GPU costs matter for self-hosting; usage and reserved-capacity terms matter for managed endpoints. No savings figure is assumed before measurement.
Map the task, quality gap, data rights, deployment constraints and ownership. Establish a prompting or retrieval baseline before committing to training.
Output: A scoped decision brief and measurable acceptance criteria.
Curate examples, agree labels and create leakage-aware splits. Compare candidate models and adaptation methods with the baseline.
Output: Versioned datasets, evaluation cases and a feasibility decision.
Run bounded experiments, inspect task errors and regressions, and assess security and privacy risks. Select a candidate only against agreed release gates.
Output: Reproducible artefacts, comparison evidence and known limits.
Integrate the selected runtime, test capacity and failure recovery, and roll out with human review or limited traffic where appropriate.
Output: A tested serving path, pilot evidence and rollback procedure.
Document data and model versions, deployment, evaluations, costs and responsibilities. Agree support, access and future change review.
Output: Agreed artefact access, runbooks and an operating plan.
Start with a paid two-week sprint to establish feasibility and build an agreed first slice. Scope, access and review points are set before starting. No long-term commitment is required; full training and production rollout may need further work.
A task owner, authorised examples, known failure cases and reviewers who can judge the result. We also need hosting constraints, expected usage and contacts for legal, security and operations decisions.
Related engineering
Explore application development and practical guidance for oversight and reliable evaluation.
Questions before you train
If the gap is current or private knowledge, retrieval or tools are often the first choice. If it is repeatable task behaviour, fine-tuning may help. They can work together, but both should be compared with a strong baseline before adding complexity.
There is no useful universal minimum. The task, model, label consistency and variety matter as much as volume. We assess a representative sample, establish a held-out evaluation and use learning curves or bounded experiments to decide whether additional data is worth collecting.
This service primarily covers adapting existing models and deploying suitable models. Training a foundation model from scratch is a substantially different data, compute and research commitment and would require a separate feasibility assessment.
No. It can improve selected behaviour, but cannot guarantee factual correctness. Use authorised sources, output checks, uncertainty handling and human review where needed. Fine-tuning also does not replace permissions or business rules.
Potentially, if the model licence, hardware, runtime and dependencies fit your requirements. We assess all processing paths, telemetry and backups. A private model endpoint alone does not prove that the entire application keeps data private.
Ownership and access are agreed for the engagement, subject to the base-model licence, dataset rights and provider terms. Open-weight adaptations and managed fine-tuning have different portability rules. We clarify artefact access and export limitations before choosing a path.
Possibly, if it meets quality and capacity requirements. The comparison includes training, hardware utilisation, infrastructure, maintenance and fallback or review work. We measure total cost at the actual workload rather than promising savings from model size alone.
Bring the task, authorised examples, current failure cases and hosting requirements. A paid two-week sprint can establish the baseline and build an agreed first slice without a required long-term commitment. Full training, evaluation and production rollout may need further work.
Start with the task
Tell us what the model needs to do, where the current approach falls short and how it needs to run. We'll define a baseline and a sensible first scope.