AI and ML Development Services
Machine learning models, NLP pipelines, computer vision, and generative AI systems built on your data and run in your infrastructure. Scoped in writing, priced before the work starts, and measured against a baseline agreed up front.
What is AI development, and why do off-the-shelf tools fall short?
AI development is the process of designing, training, and deploying machine learning models and intelligent systems that predict, classify, generate, and automate from business data. Access to models is no longer the hard part. Making them work reliably against messy real-world data, inside existing infrastructure, at production scale — that is where projects succeed or stall.
Off-the-shelf tools are trained on general data and tuned to perform acceptably across many use cases. A custom model learns from your transactions, your documents, and your customers’ actual behaviour, which means it handles your edge cases and improves as your data grows. In business-critical decisions — a missed fraud flag, a misrouted claim, a wrong classification on a document that triggers a payment — that accuracy difference carries a measurable cost.
When is custom AI worth building?
Not every problem needs a model. Five questions settle it before anything gets scoped:
Where the answers point away from AI, the honest recommendation is a rules engine, better reporting, or nothing at all — and saying so at the scoping stage costs far less than saying it after a build.
AI development services for your business
Seven service lines cover almost every engagement. Most projects start at one of the first two and move down the list as the use case proves out.
| Service | What it delivers | Best when |
|---|---|---|
| AI consulting and readiness assessment | Use-case shortlist scored on data availability and expected value, a target architecture, and a staged plan with a fixed quote | Several ideas compete for the same budget and nobody has priced them |
| Proof of concept | One use case validated against a real slice of your data, with evaluation metrics agreed before the build | A business case needs evidence before larger spend is approved |
| Custom model development | Predictive, NLP, or vision models trained on your data, with the pipelines and retraining that keep them accurate | Accuracy on your edge cases matters more than time to market |
| Generative AI and RAG systems | Retrieval pipelines over your own documents, with citations, guardrails, and evaluation against hallucination | Knowledge is trapped in documents nobody has time to read |
| AI agent development | Single-agent automation through to multi-agent workflows with tool use and human checkpoints | A multi-step process needs judgement at each step, not just a prediction |
| AI integration services | Model inference wired into ERPNext, Salesforce, HubSpot, SAP, or custom REST APIs, with auth, rate limiting, and fallback logic | A model already works but nobody can reach it from the systems they use |
| MLOps and model operations | Deployment, monitoring, drift detection, retraining, and cost tuning once the system is live | Models are in production and accuracy is quietly degrading |
What actually gets built
Underneath those services sit six system types. The right one depends on data shape and the decision being automated, not on what is fashionable.
| System type | What it does | Data it needs |
|---|---|---|
| Predictive models | Classification, regression, forecasting, anomaly and fraud detection | Hundreds to tens of thousands of labelled historical examples |
| RAG and knowledge systems | Answering questions over your own document corpus, with citations | A knowledge base in retrievable form |
| AI agents | Single-agent automation through to multi-agent workflows | Defined workflows and system access |
| Document intelligence (NLP) | Extraction, classification, summarisation, entity recognition | Domain-specific text or scanned documents |
| Computer vision | Image and video classification, detection, quality inspection | Labelled images from your own conditions |
| Speech and audio | Transcription, diarisation, voice interfaces | Representative audio samples |
Which AI platform should you build on?
Platform choice is usually settled by where the data already lives and what the security review will approve — not by model benchmarks. The trade-offs:
| Platform | Best for | Cost model | When it is the right choice |
|---|---|---|---|
| Microsoft-stack enterprises and compliance-bound workloads | Consumption, with committed-use discounts | You already run Azure, and procurement prefers a single vendor with an existing DPA | |
| Fastest route to production with the strongest general models | Per-token | Speed to market matters more than data residency | |
 AWS — Bedrock, SageMaker | Model choice inside an existing AWS estate | Per-token plus infrastructure | Your data and pipelines are already in AWS |
| Teams on GCP, strong AutoML tooling | Per-token plus infrastructure | BigQuery is already your warehouse | |
| Workloads where data cannot leave your estate | Infrastructure only, no licence | Residency rules apply, or volume makes per-token pricing expensive |
Most production systems end up hybrid: a hosted model for general reasoning, a smaller self-hosted model for high-volume or sensitive paths. The scoping session prices both before recommending one.
Technologies and frameworks
The stack is chosen per project. This is the working set most engagements draw from.
| Layer | Tools |
|---|---|
| Languages and ML frameworks | |
| LLM and agent orchestration | |
| Vector and retrieval | |
| NLP, vision and speech | |
| Model platforms | AWS Bedrock and SageMaker |
| Serving and MLOps | |
| Data and pipelines |
How much does custom AI development cost?
Published bands, before any sales call. Every engagement is scoped and quoted in writing before work begins.
| Engagement | What it covers | Timeline | Cost |
|---|---|---|---|
| Proof of concept | One well-scoped use case validated against a real slice of your data, with agreed evaluation metrics | 2–4 weeks | From $3,500 |
| Production AI system | Data pipelines, model training, API integration, and monitoring across multiple functions | 6–14 weeks | $10,000–$30,000 |
| Generative AI / large RAG | Large document corpora and multi-model architectures | Varies with data scope | Quoted after discovery |
Every driver behind these numbers is broken down in the full guide: AI Development Cost in 2026 →
Delivery process
Six stages, with a decision point at the end of the second. Work stops there if the data does not support the use case — which is cheaper for everyone than discovering it in week ten.
| Stage | Weeks | What happens | What you get |
|---|---|---|---|
| Scoping and data assessment | 1–2 | Use case defined, data readiness scored, gaps identified, target architecture chosen | A staged plan and a fixed quote |
| Baseline and evaluation design | 1 | Current performance measured, success metrics and thresholds agreed in writing | An objective bar to be judged against |
| Model development | 2–8 | Data preparation, training, iteration against the held-out test set | Weekly demos on real data |
| Integration | 2–4 | Inference wired into your systems, with auth, rate limiting, validation, and fallback paths | Model output where people already work |
| Production and monitoring | 1–2 | Deployment, accuracy and drift monitoring, alerting, runbooks | Visibility once it is live |
| Handover or managed | Ongoing | Documentation and training for your team, or retraining and tuning on a retainer | Your choice, not a lock-in |
What the evidence says about AI value
Adoption is no longer the constraint. Converting adoption into measurable value is.
That last figure is the one worth reading carefully. The gain concentrates where expertise is thinnest, which is a useful signal for choosing a first use case. None of these numbers should be dropped into an ROI model as a guarantee — impact depends on the task, the workflow, the data, and the implementation, which is why a project-specific baseline gets established before development starts.
How do you know the model actually works?
Evaluation metrics and baseline targets are set before development begins, so performance is measured objectively rather than argued about afterwards. Depending on the system that means precision, recall, F1, mean absolute error, or a business metric like processing-time reduction or approval accuracy. A test set is held out from training and validated separately, and production monitoring gives ongoing visibility into accuracy drift once the system is live.
This is the phase most quotes underestimate. A model that scores well in a notebook and fails on live inputs has not been tested — it has been demonstrated.
What enterprise AI development requires
Enterprise deployments carry requirements a pilot never meets: identity and access control, network isolation, auditability, and a vendor a security team will approve. Those constraints are cheaper to design in at architecture stage than to retrofit after a successful proof of concept — which is the most common reason a promising pilot never reaches production.
AI development: frequently asked questions
Do you build on Azure AI?
Yes — Azure OpenAI Service, Azure Machine Learning, and Cognitive Services, including deployments inside a customer's own Azure tenancy where data residency or an existing enterprise agreement makes that the requirement. Azure is frequently the right answer for Microsoft-stack organisations even when another platform benchmarks marginally better, because procurement and security approval are part of the real timeline.
Do you work with the OpenAI API?
Yes — GPT models for reasoning and generation, embeddings for retrieval, and Whisper for transcription and speech tasks. Direct OpenAI API integration is usually the fastest route to production; Azure OpenAI Service is the same family of models under different commercial and residency terms.
Which vector database should we use?
For most RAG systems the choice matters less than the chunking and retrieval strategy sitting on top of it. Pinecone is the low-operations managed default, Weaviate suits hybrid keyword-and-vector search, Qdrant is the strongest self-hosted option where data cannot leave your infrastructure, and pgvector is often sufficient when a Postgres database already exists and corpus size is modest.
How long does an AI project take?
A proof of concept for a well-scoped use case typically takes two to four weeks. A production model with integration, monitoring, and retraining infrastructure takes six to fourteen weeks depending on data readiness. Generative AI and RAG systems with large corpora take longer, because data preparation dominates the schedule.
How much data is needed?
It depends on system type. Supervised models need labelled historical examples — hundreds to thousands for simpler tasks, tens of thousands for complex ones. NLP and vision models need domain-specific text or images from your own conditions. RAG systems need a knowledge base in retrievable form. Data readiness is assessed at scoping, and gaps are identified before anything is committed.
What is the difference between machine learning and deep learning?
Machine learning covers algorithms that learn patterns from data — decision trees, gradient boosting, linear models. Deep learning is the subset using multi-layered neural networks, which is particularly effective on unstructured data like images, audio, and text. Both get used; the choice follows from data type, volume, and how interpretable the output needs to be.
Deep-dive AI services
Specialised offerings for teams whose scope narrows to one capability — pick the deep dive that matches the shape of the problem.
Related services
Know Whether the Build Is Worth It Before You Commit
A scoping session assesses your data readiness, defines evaluation metrics, identifies the right platform, and puts a fixed price on the build — yours to keep whoever builds it.