Custom AI Software Development and Machine Learning Services
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Transformative Next-Generation AI Services and Solutions
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Artificial Intelligence Solutions Powered by Human Judgment
Continuous Innovation
We embed AI into the core of how organizations innovate. Our approach encourages collaboration across teams, removes silos, and creates space for experimentation that leads to meaningful, scalable outcomes.
Digital Transformation
We position AI as a practical enabler of digital transformation, not a standalone initiative. Every solution is designed to support real workflows, customer needs, and data-driven decision-making across the enterprise.
Design Thinking
We apply design thinking to ensure AI solutions are built for people, not just systems. By starting with user context and real-world constraints, we create AI capabilities that are intuitive, useful, and adopted in practice.
Digital Responsibility
Responsible AI is non-negotiable. We design AI systems with transparency, privacy, and security at the core, helping organizations meet ethical standards and regulatory expectations.
Global Delivery Model
Our global delivery model allows us to support AI initiatives across regions and time zones. Distributed teams enable flexible engagement while maintaining consistent quality, governance, and accountability.
Selected Project Examples
AI-Powered Medical Search Platform for Clinical Accuracy
An AI-driven medical search platform designed to improve access to accurate, structured medical information. By applying semantic search and natural language understanding, the system enabled faster discovery of relevant clinical content while improving usability for healthcare professionals.
Intelligent Personal Assistance Platform for Enterprise Workflows
An AI-powered personal assistance portal to streamline information flow across the organization. The platform prioritized relevant actions, automated routine tasks, and improved employee engagement by delivering context-aware insights at the right time.
Autonomous AI-Driven Project Management System
An AI-powered project management system to support complex, distributed teams. The solution reduced manual coordination, improved decision-making through data-driven insights, and increased operational efficiency across the project lifecycle.
Buyers rarely arrive asking for a model. They arrive with a decision that takes too long, a queue of documents nobody can read fast enough, or a prototype that works in a demo and nowhere else. This page sets out the AI and machine learning work Sthenos does, the vocabulary that decides it, the stack we name, the rules it inherits, and what moves the cost.
Depending on the seniority and mix of the team. Published on our about page.
We publish no separate AI price band, because a defensible one would have to come from delivered projects.
Working software every two weeks, reviewed against the evaluation set rather than a demo.
Typical. Data readiness is the variable that decides which end of that range you are at.
On this page
- AI and machine learning services, grouped by what you need next
- The AI vocabulary, defined
- The AI stack we work in, and the categories we leave unnamed
- AI governance, security and the rules that reach an AI build
- What you receive
- How an AI engagement runs, step by step
- What moves the cost of an AI build
- What to ask any AI development company
- A short self assessment before an AI prototype goes live
- More questions about AI and machine learning
- More about AI and machine learning
- Talk to our engineers
Custom AI software development services
Custom AI software development means building AI into software made for your own data, workflows and rules, rather than configuring a product licence. At Sthenos it covers the groups set out below: choosing the use cases where AI earns its place, getting the data ready, building and validating machine learning models, generative AI applications on hosted models, retrieval over your own documents, agents that take action across your tools, and the MLOps and governance that keep a model working after launch.
Each group links to the page that sells it, and every build follows the step-by-step route described further down this page. For the firms that do this work, compared, see our guide to custom AI software development companies.
AI and machine learning services, grouped by what you need next
The groups below are ordered the way that work actually sequences, and each one links to the Sthenos page that sells it.
Deciding where AI earns its place and where a rules engine, a report or a better form would do the job more cheaply. The output is a shortlist of candidate use cases, each with the data it would need, the failure it can tolerate, and an owner. Our AI ROI calculator is free and ungated if you want to sketch the value side before you talk to anyone.
AI projects stall on data, not only on models. This is inventory, access, quality, lineage and the pipelines that keep a feature fed after launch. Sold on big data and analytics, data analytics services and data science.
Classification, regression, forecasting, ranking, clustering and anomaly detection, built and validated against a labelled set rather than a demo. This is the work behind the feature engineering, statistical analysis and model testing lines above, and it is delivered through our Python engineering and data science practices.
Drafting, summarising, extracting, classifying and answering, built on hosted models rather than trained from scratch. The engineering is in what surrounds the model: retrieval, evaluation, guardrails and the integration into the system where the work already happens. Platform work sits on Azure, Google Cloud and AWS.
Answering from your own documents and records instead of from whatever a model absorbed during training. A retrieval layer finds the passages that matter, the model writes the answer, and the answer carries its sources so a reader can check it. This is the pattern behind enterprise knowledge assistants, and it is a defensible first project because it is auditable.
Systems that take action across your tools rather than only producing text. Sold on agentic AI consulting, alongside AI automation and robotic process automation for the deterministic parts that do not need a model at all.
Versioning, deployment, retraining and the monitoring that tells you a model has quietly stopped working. This is what turns a notebook into a system somebody can operate, and it overlaps with our DevOps and cloud practices.
Access control, audit logging, approval gates, retention, and the evidence pack a security review asks for. Sold on cybersecurity, and measured independently through our production readiness audit.
Figure 1. How an AI feature is assembled
The stages are the ones this page defines above and below. Nothing here is counted, so the figure asserts structure only.
The AI vocabulary, defined
These are the words that decide an AI project, and they are used loosely almost everywhere. Each definition below is written so you can use it against any vendor, including this one.
Fine-tuning versus retrieval
Retrieval
Where we start- Leaves the model alone and hands it the relevant passages at the moment of the question.
- Cheaper, and it updates the instant a document changes.
- It can cite its sources, so a reader can check the answer.
Fine-tuning
Earns its place narrowly- Changes the model by continuing its training on your examples.
- For a consistent format or a narrow style that instructions keep failing to hold.
- It does not make a model know new facts reliably.
Supervised and unsupervised learning
Supervised learning trains on examples that carry the right answer, so it needs labelled data and it can be scored against a held-out set. Unsupervised learning finds structure without labels, which is what clustering and much anomaly detection do. The practical difference is that supervised work has a number you can argue about and unsupervised work usually does not, so it needs a human to say whether the groups it found are the groups that matter.
Embeddings and vector search
An embedding turns a passage into a list of numbers that places it near passages with similar meaning. Vector search then finds neighbours by meaning rather than by keyword, which is why it retrieves the paragraph about a refund policy when the reader typed money back. Chunk size, overlap and what you store alongside each chunk decide retrieval quality far more than the choice of store.
Prompt engineering versus tool use
Prompt engineering shapes what a model writes. Tool use lets it call something: a search, a calculation, a database query, an API in your own estate. The distinction matters commercially, because a prompt can only reformulate what it was given, while a tool call reaches live data and can change the state of a system. Anything that can change state needs permissions, logging and an approval path.
Evaluation sets and human review
An evaluation set is a fixed collection of real cases with an agreed correct answer, assembled before the build and owned by the people who do the work today. It is what makes the sentence it got better falsifiable. Human review is the second half: a sampling rate, a reviewer, and a route for a disputed answer to change either the system or the evaluation set.
Guardrails
The constraints around a model rather than inside it. Input filtering, output validation against a schema, allowed and forbidden topics, scoped permissions on every tool, rate and spend limits, and a refusal path when the system is out of its depth. Guardrails are ordinary engineering, they are testable, and they are where the safety of an AI feature actually lives.
Hallucination controls
A language model produces fluent text whether or not it has grounds for it. The controls that work are structural: ground the answer in retrieved passages, require citations and check that the cited passage supports the sentence, constrain the output to a schema where the answer has a shape, and route low-confidence cases to a person.
Model monitoring and drift
Drift is the gap that opens between the world the model was trained on and the world it now runs in. It arrives quietly, because the system keeps returning answers. Monitoring means watching input distributions, output distributions, refusal and error rates, latency, spend and the outcome metric the feature exists to move, with a threshold that pages somebody rather than a chart nobody opens.
Data privacy in prompts
Every prompt is a data transfer. The questions that decide the design are which fields may leave your boundary, whether they can be redacted or tokenised first, where the provider processes and stores them, whether they are retained or used for training, and how the transfer is logged. These are answerable in a contract and in a configuration, and they should be settled before the first integration rather than during a security review.
Cost per inference
Traditional software cost sits mostly fixed. AI cost scales with use, because inference is charged per call or per unit of text. That changes budgeting: a feature that is cheap for ten users can be expensive for ten thousand, and a retry loop with no ceiling is a cost incident rather than a bug. Model the per-call cost against realistic volume before launch, and put a hard limit on it.
The AI stack we work in, and the categories we leave unnamed
Every product named below is named because another Sthenos page already claims it, and the page that claims it is linked beside it. Where we work in a category without standardising on one product, the category is named and no product is, because a logo we have not earned is worth less to you than an honest gap.
Model platforms and hosting
- Azure OpenAI Service, Azure Machine Learning and Azure Cognitive Services, named on our Azure platform page.
- Vertex AI, named on our Google Cloud page for governed ML pipelines, deployment and monitoring.
- AWS for generative AI and MLOps pipelines, named on our AWS platform page.
- Microsoft Copilot and the wider Microsoft platform, for organisations whose AI question is really a permissions and governance question.
Classical machine learning
- Python, with Pandas, NumPy, Scikit-learn and TensorFlow, named on our Python engineering page.
- Feature engineering, statistical analysis and model validation, sold as data science.
Data platform and retrieval
- BigQuery and Azure Data Factory and Power BI, for the warehouse and the reporting layer either side of a model.
- Elasticsearch for search indexes, and Postgres and Neo4j, named on our legal industry stack.
- A vector store for embeddings. We work with the store your platform already gives you rather than adding a product to the estate, so no vector database is named here.
Agents, orchestration and evaluation
- Agent and multi-agent design, tool integration, guardrails, approvals, audit logging and evaluation harnesses, described in full on our agentic AI page.
- Agent frameworks and orchestration libraries. We use them where they earn their place and write plain code where they do not, so no framework is named as a house standard.
Delivery and operations
- Docker, Terraform, Ansible and CloudFormation, named on our DevOps engineering page.
- Kubernetes for the container platform under a served model.
- CI/CD, environments and release automation, so a model ships the same way the rest of your software does.
AI governance, security and the rules that reach an AI build
Compliance vocabulary tends to live on a company's government pages and never reach the service page a buyer actually lands on. Ours already does the work below, so here it is where an AI buyer can read it.
The frameworks a contract may name
Our public sector and cybersecurity pages catalogue FISMA, NIST SP 800-53, NIST SP 800-171, FedRAMP, StateRAMP, CMMC, CJIS, Section 508, HIPAA, SOC 2, PCI DSS and GDPR, each with its issuer and whether it is mandatory, voluntary or contractual, and our government and healthcare pages name the ones that bind those sectors. An AI feature inherits every one of these that already binds the system it sits inside. A model that reads clinical notes is handling protected health information whether or not anyone called the project a compliance project.
Figure 2. An AI feature inherits the obligations of what it sits inside
Read from the outside in. The figure restates the sentence above it and counts nothing.
Where we stand on attestation, stated plainly
We build to the controls an audit looks for, including access control, audit logging, encryption and retention, and we hand you the evidence trail. Our full list of what the company does and does not hold is published on our about page.
The four functions the NIST framework is built from
NIST organises the framework into four functions. They are an agenda for the governance conversation rather than a score, and the wording below is NIST’s own, read on the AI RMF core section at the NIST AI Resource Center. Nothing in it is counted or ranked.
Two AI-specific references worth knowing
The NIST AI Risk Management Framework was released on January 26, 2023 and is intended for voluntary use, to help organisations manage the risks AI poses to individuals, organisations and society and to build trustworthiness into how AI products and systems are designed, developed, used and evaluated.
NIST also publishes an AI RMF Playbook, and states that AI RMF 1.0 is being revised as part of the White House AI Action Plan. It is voluntary, so nobody can compel you to adopt it, and it is the most useful shared vocabulary available when a customer or a board asks how you govern an AI system.
The OWASP Top 10 for LLM and generative AI applications is the security counterpart, and its 2025 list is the one we design against: prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption. Read as a design checklist rather than a threat report, it maps almost item for item onto the guardrails, retrieval and cost-ceiling decisions described further up this page.
What you receive
Artefacts, not slideware. Every item below is a thing your own team can pick up and use after we leave.
- A use case shortlist, each entry with its data dependency, its tolerance for a wrong answer and a named owner.
- A data readiness note saying what exists, what is usable, and what has to be fixed before anything is trained or retrieved.
- An evaluation set of real cases with agreed correct answers, and the baseline showing how the current process performs.
- A reference architecture: where data flows, where it stops, which components are yours and which are the provider's.
- The build itself, in your repository, with tests and the evaluation suite wired into the pipeline.
- A guardrail specification: permissions per tool, validation on every output, spend and rate limits, and the refusal path.
- Monitoring and alerting configuration covering quality, drift, latency, error rate and spend.
- A prompt, model and dataset version record, so an answer given last month can be explained this month.
- The security and privacy evidence pack: data flows, retention, access model and audit log design.
- A runbook and a handover session, written for the team that will operate it rather than for the team that built it.
How an AI engagement runs, step by step
We work in two week sprints, so working software arrives every two weeks and a first production release typically lands in three to six months. Those two figures are published on our about page and they apply here.
- DiscoveryThe workflow, not the model. We map how the work moves today, where it costs time, and what a good answer looks like to the people doing it. Output: the use case shortlist and a recommendation, including the recommendation not to build.
- Data and feasibilityWhat data exists, who owns it, whether it is fit to act on, and what leaves your boundary. Output: the data readiness note and the reference architecture.
- Evaluation firstThe evaluation set and the baseline are built before the feature. If we cannot agree what correct means, that is the finding, and it is cheaper now than after the build.
- Build, in two week incrementsRetrieval, model integration, guardrails and the surrounding application, reviewed at the end of every sprint against the evaluation set rather than against a demo.
- Integration and security reviewThe systems it has to reach, the permissions it runs under, the logging, and the evidence pack your reviewers will ask for.
- Production releaseMonitoring, alerting, spend ceilings, rollback, and a defined human path for the cases the system should not decide alone.
- Operate and improveRetraining or prompt and retrieval changes driven by monitored outcomes, with the evaluation set updated as reality moves.
What moves the cost of an AI build
Our rates are $150 to $250 per hour, depending on the seniority and mix of the team, and a typical project runs $50,000 to $200,000. Both figures are published on our about page. We publish no separate AI price band, because a defensible one would have to come from delivered projects rather than from a market average. What we can give you is the list of things that actually move the number.
| Cost driver | What it changes |
|---|---|
| Data readiness | Clean, accessible, documented data is a different project from data spread across four systems that disagree. |
| Which technique the problem needs | A retrieval feature on hosted models is not the same commitment as training and validating your own model. |
| Level of autonomy | Suggesting to a person is cheap. Acting without one is expensive, because the permissions, approvals, logging and rollback are the work. |
| Integration count, and whether the counterpart system is documented | An undocumented internal API is a common source of an overrun. |
| Evaluation depth | How many labelled cases, produced by whom, and how often re-scored. |
| Security and compliance requirements | The control framework named in your contract decides how much evidence has to be produced alongside the software. |
| Running cost | Inference volume, response length, retries and how much context each call carries, which is a monthly cost rather than a build cost. |
| Who operates it afterwards | A handover to your team costs less over a year than a dependency on ours, and we would rather build the first. |
What to ask any AI development company
- What would make you tell me not to build this? A vendor with no such answer has never given it.
- How will we know it works, and who agrees the correct answers? Listen for an evaluation set and a baseline, not a demo.
- Where does our data go, is it retained, and is it used to train anything? Ask for it in the contract, not in the sales call.
- Which parts run on a hosted model, and what happens when that provider changes the model or its price?
- What does this cost to run at full volume, not at pilot volume, and where is the ceiling set?
- Who can see the audit log, and can you reconstruct why the system answered the way it did three months ago?
- What breaks first when the data drifts, and what alerts on it?
- Who owns the code, the prompts, the evaluation set and the cloud accounts at the end?
- What happens if the model is wrong in a way that reaches a customer or a regulator?
- Who operates this after handover, and what does your team have to learn to do it?
A short self assessment before an AI prototype goes live
Score yourself honestly. Anything you cannot evidence counts as a no.
- There is an evaluation set of real cases with agreed correct answers, and a baseline for the current process.
- Someone has written down what the system must never do, and a test proves it does not do it.
- Every tool the system can call runs under scoped permissions rather than an administrator credential.
- Actions that are consequential or hard to reverse sit behind a human approval.
- Every request and response is logged with enough context to reconstruct a decision months later.
- Prompts, model versions and datasets are versioned, so a change in behaviour can be traced to a change in the system.
- There is a spend ceiling and a rate limit, and someone is alerted before either is reached.
- Personal or regulated data in prompts has been identified, and the retention and training terms are written down.
- A person owns the outcome, and there is a route for a user to dispute an answer.
- There is a rollback: the previous version can be restored and the feature can be switched off without a deployment.
If you would rather someone else scored it
The full measurement is our seven step production readiness playbook, which scores 29 checks across seven areas from 0 to 4 on evidence rather than opinion. For the security half specifically, work through The AI-Built Prototype Security Checklist (25 Points). If you would rather someone else scored it, that is our production readiness audit.
Guides on this topic
- Best AI agent frameworks
- AI-powered software development companies
- Agentic AI consulting for enterprise
- AI custom software development companies
- Vertical and horizontal AI agents
- AI agent development companies
- Multi-agent systems
- AI-augmented software development
- Conversational AI in insurance
- Benefits of AI in software development
- AI in healthcare
- AI-driven development
- AI in financial services
- AI for government agencies
- AI-first architecture
- First AI automation
- AI custom software development
- AI-first systems
- AI agent framework
- Choose an AI software development company
- Multi-agent system
- Conversational AI
- How agentic AI will change work
- What agentic AI is
- AI-first IT service providers
More questions about AI and machine learning
What is retrieval augmented generation?
Retrieval augmented generation, usually shortened to RAG, answers a question by first finding the relevant passages in your own documents and records, then asking a language model to write the answer from those passages. It keeps answers current, because updating a document updates the answer, and it lets every answer carry its sources so a reader can check it.
Do you fine-tune models?
Only when retrieval and instructions have been tried and the problem is genuinely a matter of consistent format or narrow style. Fine-tuning is a real commitment: it needs curated examples, a held-out evaluation set and a plan for repeating the exercise every time the base model moves. It does not reliably teach a model new facts, which is the job retrieval does better and more cheaply.
How do you keep our data out of public models?
By design and by contract rather than by promise. We identify which fields may leave your boundary, redact or tokenise what does not need to, choose a deployment where processing and storage locations are stated, disable retention and training on your content where the terms allow it, log every transfer, and put the whole data flow in the evidence pack your security reviewers read.
What does an AI project cost?
Our rates are $150 to $250 per hour, depending on the seniority and mix of the team, and a typical project runs $50,000 to $200,000. We publish no separate AI band, because we would have to derive one from delivered projects rather than from a market average. The cost drivers section above lists what actually moves the number, and discovery is deliberately short so you get a costed roadmap early.
How long does an AI project take?
We work in two week sprints, so working software arrives every two weeks, and a first production release typically lands in three to six months. A narrow retrieval feature on data that is already accessible reaches production faster than a model that has to be trained and validated. Data readiness is the variable that decides which of those you are in.
Can you take an AI prototype to production?
Yes, and it is a distinct piece of work rather than a continuation of the prototype. The prototype proved the idea; production needs evaluation, guardrails, permissions, logging, monitoring, spend ceilings and a rollback. Our seven step production readiness playbook is the measurement we use, and it scores 29 checks across seven areas on evidence rather than opinion.
Do you build AI agents?
Yes. Agents are systems that take action across your tools rather than only producing text, and they carry a different risk profile because they can change the state of a system. That work has its own page, including the governance, approval gates and audit logging an agent needs before it is allowed near production.
More about AI and machine learning
Ready to talk? Tell us the decision that takes too long, or the prototype that works in a demo and nowhere else, and we will tell you whether AI is the right instrument for it. Talk to our engineers.
