How much does it cost to build an AI application?

A focused pilot typically costs $12k–$35k. Production features range from $35k to $120k depending on integrations, security and scale. Model API usage is a separate running cost that we estimate during the pilot.

Is our data safe when using AI models?

Yes, when set up correctly. We use enterprise API agreements or private deployments where your data is not used for model training, enforce access controls, and log every request.

What is RAG and do we need it?

Retrieval-augmented generation lets an AI model answer from your own documents instead of only its training data. If you want accurate, cited answers about your products, policies or knowledge base, RAG is usually the right approach.

Can AI agents take actions in our systems?

Yes, through approved APIs with strict permissions, logging and approval steps for important actions. We start with read-only or low-risk actions and expand as trust grows.

How do you measure if the AI is working?

We build an evaluation set from real examples, measure accuracy, cost and response time, and track the business metric the project was meant to improve.

Is our data safe when using AI models like ChatGPT or Claude?

We use business API tiers that do not train on your data, keep sensitive data in your own cloud where needed, mask personal information and log every request so you stay in control.

AI Development Services | Generative AI & Data

AI development that solves real business problems

AI development services help companies apply artificial intelligence — large language models, machine learning, computer vision and natural language processing — to real workflows. Data engineering is the foundation underneath: collecting, cleaning, storing and governing the data those models depend on.

We focus on AI that earns its place: answering customer questions accurately, drafting documents, extracting data from files, routing tickets, forecasting demand and spotting anomalies. Every project starts with a measurable goal and a small pilot before any large investment.

Our principle: start with the business outcome, prove it with a pilot on your real data, then scale what works — with security, cost controls and human oversight built in.

Where AI delivers value today

Customer support

AI assistants that answer from your help center and policies, and hand over to humans when needed.

Document processing

Extract data from invoices, contracts, forms and emails into your systems automatically.

Internal knowledge search

Ask questions across wikis, drives, tickets and manuals and get cited answers.

Sales & marketing

Lead scoring, personalized content drafts and CRM data enrichment.

Operations

Demand forecasting, anomaly detection, scheduling and quality inspection.

Software teams

Code assistants, test generation and automated analysis of logs and incidents.

Our AI & data services

Generative AI development

Applications built on large language models from OpenAI, Anthropic, Google and open-source models, with prompt design, evaluation and guardrails.

AI agent development

Agents that complete multi-step tasks — researching, updating systems and calling APIs — with permissions, logging and approval steps.

RAG development

Retrieval-augmented generation that grounds AI answers in your documents and data, with source citations and access control.

Machine learning development

Classification, prediction and recommendation models trained on your data and deployed with monitoring.

NLP development

Text classification, entity extraction, summarization and sentiment analysis for support, legal and research workflows.

Computer vision

Image and video models for inspection, counting, document capture and safety monitoring.

Data engineering

Pipelines that move and transform data from your apps into warehouses and lakes, reliably and on schedule.

Data analytics & BI

Dashboards and reporting in Power BI, Looker or custom tools, built on a trustworthy data model.

Responsible, secure AI

Your data is not used to train public models; we use enterprise API terms or private deployments.
Role-based access so AI only retrieves documents a user is allowed to see.
Evaluation sets that measure accuracy before and after every change.
Guardrails against prompt injection, data leakage and off-topic answers.
Human-in-the-loop review for high-impact actions.
Usage and cost monitoring per feature and per user.

Technology we use

ModelsOpenAI GPT, Anthropic Claude, Google Gemini, Llama and other open models

FrameworksLangChain, LlamaIndex, PyTorch, TensorFlow, scikit-learn

Vector searchpgvector, Pinecone, Elasticsearch, OpenSearch

DataPostgreSQL, BigQuery, Snowflake, dbt, Airflow

CloudAWS Bedrock, Azure OpenAI, Google Vertex AI

From idea to production AI

Use-case workshop1 week

Identify and rank AI opportunities by value, feasibility and risk.
Data readiness check1–2 weeks

Review data sources, quality, access and privacy constraints.
Pilot / proof of concept3–6 weeks

A working prototype on your real data with measured accuracy and cost per task.
Production build6–12 weeks

Integration with your systems, security, monitoring, evaluation and user training.
ImproveOngoing

Track quality and cost, retrain or re-prompt, and expand to new use cases.

AI development cost

Engagement	Scope	Timeline	Typical budget (USD)
AI pilot	One use case, real data, measured results	3–6 weeks	$12k – $35k
Production AI feature	Assistant, RAG search or document AI in your product	2–4 months	$35k – $120k
Data platform	Pipelines, warehouse and BI dashboards	2–5 months	$40k – $150k

Ranges are typical for projects of this kind and are shared to help you budget. Your fixed quote comes after a free discovery call and depends on scope, integrations and timeline. Model usage (API) costs are billed separately by the provider and estimated during the pilot.

Choosing the right AI approach

Approach	What it is	Best for
Prompt engineering	Using a general model with well-designed instructions	Drafting, summarizing, classification with little data
RAG	Model answers using documents retrieved from your data	Accurate answers about your products, policies and knowledge
Fine-tuning	Further training a model on your examples	Consistent style or very specific tasks at scale
Classic machine learning	Models trained on structured data	Forecasting, scoring, fraud and anomaly detection
AI agents	Models that plan steps and use tools/APIs	Multi-step tasks across systems with oversight

Most successful projects start simple — good prompts and RAG — and add complexity only when measurements show it is needed.

Data engineering: the foundation for AI and analytics

AI and reporting are only as good as the data underneath. Our data engineers build pipelines that pull data from applications, SaaS tools and files; clean, validate and model it; and load it into a warehouse such as BigQuery, Snowflake or PostgreSQL on a reliable schedule. We add data-quality tests, lineage, access controls and documentation, so analysts trust the numbers and AI features retrieve the right information.

Common AI pitfalls we help you avoid

No clear success measure — we define accuracy, cost and business targets before building.
Messy or inaccessible data — a data-readiness check comes first.
Hallucinated answers — grounding with RAG, citations and evaluation sets reduce them.
Unexpected running costs — model choice, caching and usage limits keep costs predictable.
Security gaps — permissions and prompt-injection defences are built in, not added later.

How long does an AI project take?

Successful AI projects move in short, measurable stages. Each stage ends with a clear go or no-go decision, so you never invest heavily in an idea that does not work on your data.

Stage	Typical duration	Outcome
Discovery and data review	1–2 weeks	Use case shortlist, data readiness assessment, success metrics
Proof of concept	2–4 weeks	Working prototype tested on real examples, with measured accuracy
Pilot	4–8 weeks	Integrated into one team’s workflow, with guardrails and monitoring
Production rollout	4–12 weeks	Secure, scalable deployment, user training and ongoing evaluation

Typical durations for planning purposes. Your written estimate sets the real timeline after discovery.

AI use cases by department

These are the use cases we see deliver value fastest, because the data already exists and the result is easy to measure:

Department	Use cases	How success is measured
Customer support	AI assistant over help docs, ticket triage, reply drafting	Resolution time, deflection rate, CSAT
Sales	Lead scoring, call and email summaries, proposal drafting	Win rate, time spent on admin
Finance	Invoice and receipt extraction, anomaly detection	Processing time, error rate
Operations	Demand forecasting, document processing, scheduling	Forecast accuracy, hours saved
HR and internal	Policy Q&A assistant, onboarding helper, CV screening support	Questions answered, time to productivity

Measuring quality: evaluation and guardrails

Large language models can be impressively right and confidently wrong. We treat quality as an engineering problem with tests, not a matter of trust:

A test set of real questions and expected answers, agreed with your team before building
Automated evaluation on every change to prompts, models or data
Answers grounded in your documents, with sources shown to users
Guardrails that block off-topic, unsafe or confidential output
Human review steps for high-impact actions such as payments, approvals or customer emails
Production monitoring of cost, latency, accuracy and user feedback

This is what turns a promising demo into a system your team can rely on — and lets you switch models later as better or cheaper ones appear.

Is your data ready for AI?

Most AI projects are limited by data, not models. Before committing budget, check honestly where you stand:

Question	Ready	Needs work
Where does the data live?	In known systems you can access through APIs or exports	Spread across inboxes, personal drives and spreadsheets
Is it labelled or structured?	Key fields are consistent and complete	Free text with inconsistent categories
Who owns it?	A named owner who can approve its use	Nobody is sure who can say yes
Is its use permitted?	Privacy and contractual terms allow the intended use	Unclear consent or customer agreements
Is there enough history?	Months or years of relevant examples	Only a few recent records

Anything in the right-hand column is fixable, but it shapes the plan. Often the first phase is data engineering rather than modelling.

How to evaluate an AI development partner

Do they start with the business problem and a success measure, or with a favourite model?
Can they explain how they will evaluate quality before and after launch?
How do they protect your data — where is it processed, stored and logged?
What happens when the model is uncertain or wrong — is there a human in the loop?
Will you own the prompts, code, evaluation sets and any fine-tuned models?
How will costs per request or per prediction be tracked and controlled?

Be cautious of anyone promising precise accuracy figures before seeing your data. Honest partners give ranges after a short proof of concept.

AI terms for decision-makers

Term	Meaning
LLM	A large language model that understands and generates text
RAG	Retrieval-augmented generation: the model answers using documents retrieved from your own sources
Fine-tuning	Further training a model on your examples to specialise it
Evaluation set	A fixed collection of test cases used to measure quality over time
Guardrails	Rules and checks that stop unsafe or off-topic outputs
Drift	Gradual decline in model accuracy as real-world data changes

See RAG development and machine learning development for specific approaches.

Why work with us on AI

Software engineers first — we build AI into real products, not demos.
Pilots with measurable success criteria before you commit to a full build.
Model-agnostic: we pick the right model for quality, cost and privacy.
Full-stack delivery including the web or mobile interface your users need.

Explore more in Software Development Services.

AI Development & Data Engineering Services