LLM Development Services, Measured Before They Are Tuned

Which model, prompted how, tuned or not, measured against what, and at what cost per request. Our LLM development services build the engineering underneath your AI feature, starting with the evaluation harness that turns every later decision from an opinion into a measurement.

Get an Evaluation Harness Scoped

Prompting, tuning, or neither

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What Our LLM Development Services Cover

Our LLM development services work on the model layer, and an evaluation harness comes first, because without one every later decision is opinion. We run these systems in production with human review and feedback loops behind them. A formal groundedness harness delivered as a standalone piece of client work is not something we have shipped before, so it is scoped and priced as new work rather than presented as proven elsewhere. Any supplier claiming otherwise is worth asking which client it was built for and what it measured.

The work covers model selection benchmarked against your own inputs rather than public leaderboards, prompt engineering held in version control rather than tuned by feel, a costed judgement on whether fine-tuning earns its place, the harness that measures any of it, and inference routing that keeps unit cost predictable as volume grows.

Retrieval, the product a user touches and the conversation design behind a bot are separate disciplines with their own failure modes. We build those too, and scoping them separately is what keeps an estimate defensible instead of vague.

If what you need is the product rather than the layer · If the model needs to answer from your own material

Our LLM Engineering Team, in Numbers

The engineers and delivery record behind the LLM work described here.

60+

AI Engineers

50+

AI Solutions Delivered

80+

AI-Integrated Workflows

30+

Industries Served

95%

Client Retention

Our LLM Development Services, and What Each One Delivers

Each service below can be engaged on its own. The evaluation harness makes everything else measurable, and commissioning any of the rest without it is how teams end up tuning by anecdote.

Evaluation Harness Construction

We build a fixed set of your real inputs with agreed acceptable outputs, including the cases the system should decline, and wire it to run automatically against every change. Without it, every other decision on this page is opinion and improvement cannot be told apart from change.

Model Selection on Evidence

We benchmark candidate large language models against your harness on your own inputs, measuring cost per request and latency alongside accuracy. What we usually find is that a cheaper model handles most of the traffic and the expensive one is needed only for a minority we can route.

Prompt Engineering and Versioning

We rewrite your prompts as versioned, reviewed and tested artefacts, with worked examples and structured output rather than freeform instruction. It usually replaces a long prompt nobody owns that has accumulated instructions from four people over six months.

LLM Fine-Tuning and Model Customisation

We tune a model on your own examples once prompting has been tried properly and demonstrably fails. We test the cheap route first and show you the result either way, because tuning is needed far less often than the market implies.

Inference Cost and Routing Design

We make per-request cost visible, add caching where it applies, and route simple traffic to a cheaper model so the hard traffic does not set your whole bill. Generative features get more expensive as they get more popular, which is the reverse of normal software economics.

Provider Abstraction and Migration

We keep the model behind an interface and move you between providers when pricing, deprecation or behaviour makes it sensible. It stops a provider’s roadmap becoming your unplanned project, on a schedule you do not control.

Guardrails and Output Validation

We add structural validation of what comes back, constraints on what may be asserted, and defined behaviour when the model returns something unusable. It is not a substitute for the harness: guardrails catch the malformed, and only evaluation catches the plausible and wrong.

LLM Projects We Have Delivered, and What the Harness Showed

The numbers worth comparing on these are what accuracy did against the fixed test set, and what happened to cost per request. Moving one at the expense of the other is easy, and it is not an improvement.

See All Work

How Engineering Leads Describe the Work

Published on platforms that verify an engagement before a review appears.

Reviewed on GoodFirms
We have contracted a developer from Aipxperts now for several months, based on a referral. We have been very pleased with the quality of the work, the knowledge and skill level of our developer, and the value we're receiving for our fee. We also very much appreciate that the development team works at night (effectively), so we are sometimes able to turn client requests around in a day.There have been a couple of situations where we needed urgent help outside of our developer's normal business hours, and we've received that help (for which I am very grateful). While we have some challenges with communication sometimes, our overall satisfaction level is very high.
Jason LancasterPresident, Spork Marketing
Reviewed on Upwork
Our experience working with Aipxperts has been exceptionally satisfying. From start to finish, they handled the project with professionalism and responsibility. Communication was seamless, and they effectively addressed our requirements, delivering high-quality results on time. Their technical expertise was particularly impressive, as they effortlessly solved complex problems. We highly recommend Aipxperts for their outstanding service and dedication to client satisfaction.
Full-Stack Developer Needed for Angular 15 and NestJS ProjectVerified Upwork client

LLM Development by Industry, and What Belongs in Your Test Set

Each entry names the LLM work we most often deliver in that industry and the failures that have to be in its evaluation set, because a harness missing those measures the wrong thing very well.

Fintech and financial services

Cases where the model must refuse rather than estimate, and cases where a number has to be exactly right or not offered. In fintech and financial services, refusal correctness carries more weight than accuracy does.

Education and EdTech

Age-appropriateness and factual accuracy on material a learner will treat as authoritative. For education and EdTech, the test set has to include the confidently wrong answers, which are harder to generate than the correct ones.

Telecom

Volume is the constraint, so cost per request dominates the design and routing matters more than model choice. At telecom scale a fractional saving per call becomes the whole business case.

On-demand platforms

Short interactions at very high volume, where latency and cost per request constrain the model choice more than quality does. On on-demand platforms, routing simple traffic away from the expensive model is usually the whole optimisation.

Retail

High-volume product and category text where consistency matters more than brilliance. For retail, the test set is mostly about register and format rather than about facts.

Mobile games

Tone and world consistency across large volumes of generated text, plus localisation. The mobile games evaluation set is unusually subjective and needs human raters rather than automated scoring alone.

Healthcare administration

Anything a reader could construe as clinical guidance belongs in the refusal set rather than in the accuracy set. In healthcare administration that distinction shapes the whole harness.

What Aipxperts Commits To on Every LLM Build

Every LLM development company promises good work, so promises are worth nothing on their own. Each of these commitments limits what we are allowed to propose, which is a different thing from a promise and worth reading if you are comparing suppliers.

01You get a measurement before anything is changedThe harness comes first and every subsequent decision is reported against it. Teams without one improve their systems by anecdote, and anecdote is why so many of these projects consume budget without producing progress.

02We try the cheap route first and show you what it achievedPrompting and structured examples before tuning, and a smaller model before a larger one. We report what the cheap route achieved even where it succeeded and cost us the larger engagement.

03Your build survives a provider changing its mindPricing changes, versions get deprecated and behaviour shifts, all on a schedule set by somebody else. A system wired directly to one provider converts each of those into an unplanned project.

04You see cost per request before volume makes it a problemPer-request cost is measured from the first build and routing is designed rather than retrofitted. A feature that is comfortable at pilot volume and uncomfortable at scale is a design failure rather than a surprise.

05We are straight about what we build and what we buyThese are commercially available models, selected, prompted, occasionally tuned and engineered around. Any supplier at this scale implying they build models from the ground up is describing a different business.

Prompting, Tuning or Retrieval, and How to Tell Which One You Need

The same complaint gets three different fixes proposed, and choosing the wrong one is expensive. The table sorts them by the symptom that identifies each, with retrieval the one most often needed and least often offered.

SymptomThe interventionRelative costWhere it is done
Output is in the wrong format or registerPrompting and worked examplesLowestHere
It states things about your business that are untrueRetrieval, not tuningLowRAG page
It cannot follow a specialist convention no amount of instruction conveysFine-tuningHighHere
It is right but too slow or too expensiveModel routing and cachingLow to moderateHere
Quality varies and nobody can say whether changes helpedThe evaluation harnessLow, and it comes firstHere
It behaves differently since last monthProvider abstraction and re-evaluationModerateHere

The LLM Tech Stack We Build On

The providers, orchestration, evaluation and observability tools our engineers build LLM systems on. What is listed changes as the market does, which is precisely why the architecture keeps it swappable.

Languages

pythonPythontypescriptTypeScriptSQL

Foundation Models

openaiOpenAI GPTanthropicAnthropic ClaudegooglegeminiGoogle GeminimetaLlamamistralaiMistral

Orchestration and Prompting

langchainLangChainlanggraphLangGraphLlamaIndexhaystackHaystackSemantic KernelDSPy

Fine-Tuning

huggingfaceHugging Face TransformershuggingfacePEFT and LoRAQLoRApytorchPyTorchDeepSpeedAccelerate

Vector and Retrieval

PineconeWeaviateqdrantQdrantmilvusMilvuspgvectorChromaelasticsearchElasticsearchopensearchOpenSearchFAISS

Evaluation and Red-Teaming

RagasDeepEvalLangSmithArize PhoenixweightsandbiasesWeights & Biases

Serving and Inference

vllmvLLMnvidiaNVIDIA TritonrayRay ServebentomlBentoMLONNX RuntimeamazonwebservicesAmazon BedrockmicrosoftazureAzure OpenAI ServicegooglecloudGoogle Vertex AI

MLOps and Observability

mlflowMLflowKubeflowLangfuseopentelemetryOpenTelemetryprometheusPrometheusgrafanaGrafana

Cloud and Infrastructure

amazonwebservicesAWSmicrosoftazureMicrosoft AzuregooglecloudGoogle ClouddockerDockerkubernetesKubernetesterraformTerraformfastapiFastAPI

Ratings on the Review Platforms

Gathered on Clutch, GoodFirms, Upwork and Google, across web, mobile and enterprise delivery since 2012.

Upwork4.8150 reviewsClutch5.012 reviewsGoogle4.335 reviewsGoodFirms5.05 reviews

What the Model Sees, and What You Keep

The boundaries that matter on LLM work are settled in the design rather than defended at a review, defined per engagement and written down rather than assumed from a provider’s default settings.

What goes to a providerOnly what the task requires, with personal data minimised or stripped by design. The field-level decision is made at design stage and written into the deliverable rather than described verbally.Your prompts, examples and evaluation sets are yoursThey are the accumulated intelligence of the engagement and frequently outlast the code around them. Ownership is assigned on creation rather than at final payment, and any tuned artefact belongs to you.Training, and the setting that has to be checkedNothing you send becomes training data for a general model. Where a provider allows that by default, the configuration disabling it is applied and the evidence handed over.On certification, and what matters more on this layerAipxperts holds neither ISO 27001 nor SOC 2 and no provider partner status. On model-layer work the evidence worth having is the harness itself and the record of what each change did to it, both of which live in your environment.

How Model-Layer Work Runs, and What Each Stage Establishes

The order is deliberate and slightly unfashionable. Measurement before improvement, and the cheap intervention before the interesting one.

01Failure collectionReal examples of output that was wrong, in the words of whoever noticed. Redacted is fine. It establishes what the harness has to contain, drawn from reality rather than from imagination.02Harness constructionFixed inputs, agreed acceptable outputs, refusal cases, and automated scoring where scoring is possible. This sets the baseline, and everything after it is reported against that.03Baseline measurementThe current system scored honestly, including on the cases nobody had thought to test. You find out how bad it actually is, which is usually different from how bad it feels.04Prompt and structure workRestructured prompting, worked examples and output schemas, tested against the harness. This shows how much of the gap closes for very little money, and it is frequently most of it.05Model comparison and routingCandidate models scored on your inputs, with cost and latency measured alongside quality. It answers whether the expensive model is needed for all traffic or only for a routable minority.06Tuning, only if the evidence supports itUndertaken when prompting has demonstrably failed, with the baseline available to prove what tuning delivered. Whether the expense was justified is answerable only because the harness was built first.07Monitoring and re-evaluation on provider changeThe harness runs on a schedule and on every provider change, including ones you did not initiate. It means a silent behaviour change is caught by a test rather than by a customer.

LLM Development Questions, Answered for Engineers

Written for an engineering lead rather than a commercial one, because this is the page an engineering lead ends up on.

Share your project vision

Tell us what you want to build. A specialist, not a salesperson, replies.

PDF, DOC or image, up to 10MB. Optional.
My idea is confidential – happy to sign an NDA.

Mostly harness construction and measurement rather than model work. That surprises people who priced the modelling. The cheapest useful engagement here is a harness and a baseline, and it is frequently all a team needs to fix things themselves.

Probably not, and the way to find out is cheap. Prompting with structured examples closes most of what arrives described as a tuning problem. Where it genuinely does not, you will have the evidence and a baseline, which is a better position than tuning on a hunch.

Whichever wins on your harness against your inputs at an acceptable cost and latency. Model reputation and benchmark scores are weak predictors of performance on a specific task, and the comparison takes days rather than weeks once a harness exists.

A fixed set of real inputs, the outputs you would accept for each, the cases the system should refuse, and automated scoring where the task allows it. Once it exists, every change becomes measurable and arguments about quality become arithmetic.

Measure per request, route simple traffic to a cheaper model, cache what repeats, and cap what runaway usage can spend. Generative features get more expensive as they succeed, so the arithmetic belongs in the design rather than in the first large invoice.

The interface absorbs it and the harness tells you whether the replacement behaves the same. Without both, that event is an unplanned project on somebody else’s timetable, which is the commonest way these systems break.

Not from this layer. If the information is not in the model, no prompt and no tuning will put it there reliably. That is retrieval, and it belongs on the RAG page.

No. These are commercially available models, selected, prompted, occasionally tuned and engineered around. Building a foundation model is a different order of undertaking and no supplier at this scale is doing it.

You do, from creation. On this layer they are the durable asset. The code around them is replaceable, and a good engagement leaves your team able to run the harness without us.

When you need a finished product rather than the model layer beneath it, when the real problem is that the model does not know your material, or when what you actually want is a conversation. Each of those is a different build with its own page.

Scope Your LLM Development From Real Outputs

A handful of real failures, redacted as needed, become the opening entries in an evaluation set and tell us more than a specification would. Back comes what the harness would need to contain, what the baseline is likely to show, and which of the interventions on this page your problem probably needs.

Send Us Ten Bad Outputs

Prompting, tuning, or neither

Published Work on the Model Layer

Harness design, routing economics and provider migrations, written up by the engineers who ran them.

The AI Services That Sit Around LLM Work

LLM work rarely arrives on its own. Retrieval, the product a user touches and the conversation design behind a bot are separate disciplines with their own failure modes, and we build those too. Scoping them separately is what keeps an estimate defensible rather than vague.

Generative AI DevelopmentRAG DevelopmentAI Chatbot DevelopmentAI DevelopmentMachine Learning DevelopmentAI Agent Development