As a custom AI development company, CodeBase Coders designs, builds and integrates AI software that fits your data, systems and security requirements: generative AI applications, AI agents, machine learning models, chatbots, copilots and intelligent automation. One engineering team takes you from proof of concept to a monitored production release.
Good AI development is mostly good engineering. Our AI development services combine data engineering, model work and full-stack software development, so the model, the application around it and the integrations it depends on are designed together and tested together.
Pick a single service or combine several. Every engagement is delivered by the same cross-functional team, covering AI engineers, backend and front-end developers, QA and DevOps, so nothing is lost between strategy, build and launch.
AI product discovery and scoping
Proof of concept and AI MVP
AI-powered SaaS development
AI features in web and mobile apps
We build AI-first products and SaaS platforms end to end: product discovery, UX for AI features, model selection, backend and front-end development, usage-based cost controls and a release plan that grows with your users.
RAG over company knowledge
LLM fine-tuning and evaluation
Content and report generation
Multimodal text, image and audio apps
As a generative AI development company, we build LLM-powered applications that write, summarise, search and reason over your content. We combine RAG, prompt design and fine-tuning where each makes sense, and test outputs for accuracy, tone and safety before release.
Single-task and workflow agents
Multi-agent orchestration
Tool use and function calling
Approval steps and action logs
We build agentic AI systems that break a goal into steps, call your APIs and tools, verify results and escalate exceptions, automating work like order handling, research, reconciliation and onboarding while keeping every action logged.
Customer service chatbots
WhatsApp and messenger bots
Lead qualification bots
Employee helpdesk assistants
We develop AI chatbots for websites, apps, WhatsApp and internal portals that answer from your own knowledge base, complete requests such as bookings and order checks, and pass conversations to your team with full context.
Inbound call assistants
Appointment and reminder calls
Speech-to-text and text-to-speech
Call summaries for agents
Voice agents that answer and place calls, understand natural speech, look up information in your systems and complete simple tasks such as appointment booking or status updates, handing over to a person when a call needs one.
In-app assistants for your product
Sales and CRM copilots
Developer and support copilots
Analytics copilots for plain-language queries
Copilots embedded in the tools your team already uses that draft emails and documents, answer questions about records, suggest next actions and explain data, grounded in your information and limited to what each user is allowed to see.
Demand and revenue forecasting
Recommendation systems
Fraud and anomaly detection
Risk, churn and lead scoring
Custom machine learning development for prediction and pattern problems: forecasting, classification, scoring, recommendation and anomaly detection, with feature engineering, explainability and monitoring so models stay trustworthy after launch.
LLM API integration
CRM, ERP and helpdesk AI add-ons
Legacy application AI enablement
Secure data connectors
We connect AI models and platforms to your existing software stack, including Salesforce, HubSpot, SAP, Odoo, Shopify, Zendesk and custom systems, with secure APIs, queues and middleware, so AI works inside current workflows without a rebuild.
Document capture and validation
Email and ticket triage
AI-enhanced RPA workflows
Exception handling queues
Intelligent automation combines rules-based RPA with AI that can read documents, understand emails and make simple decisions, so end-to-end processes like invoice handling, claims intake or onboarding run with far less manual effort.
AI opportunity assessment
Data readiness review
Model and cost comparison
Time-boxed proof of concept
Before a full build, our AI consultants help you choose the right use case, check data readiness, compare models and costs, and prove feasibility with a short proof of concept on your real data, so the investment decision rests on evidence.
Model CI/CD and versioning
Drift and quality monitoring
LLM usage and cost tracking
AIOps for IT operations
We set up the operations layer AI needs in production: CI/CD for models and prompts, evaluation on every change, drift and quality monitoring, cost dashboards and retraining, plus AIOps that applies AI to your own IT monitoring and incident handling.
A short discovery session with our AI engineers can save months of building the wrong thing. We will review your use case, data and constraints and suggest the most practical way to build it.
We pick the technique that fits the problem rather than forcing every project onto the newest model. These are the AI technologies our development teams work with most.
Supervised and unsupervised models for forecasting, classification, clustering and scoring on structured data.
Large language and multimodal models that create text, summaries, images and code.
Goal-driven agents that plan, call tools and complete multi-step tasks with oversight.
Grounding LLM answers in your documents and databases, with citations and permission checks.
Image and video understanding for OCR, inspection, detection and tracking.
Extraction, classification, sentiment and entity recognition across text and speech.
Rules-based automation for repetitive system tasks, extended with AI decisions.
Exploratory analysis, experiments and dashboards that show where AI will pay off.
Compact models that run on devices and on-site hardware for low latency and privacy.
Techniques that show why a model made a decision, important for regulated and high-stakes use.
Speech recognition, synthesis and voice agents for calls and voice interfaces.
Right-sized models, caching and batching that cut compute cost and energy use.
AI systems handle sensitive data and make consequential suggestions, so we design with recognised frameworks in mind from the first sprint. We align our engineering with the standards that apply to your project; certification of your organisation remains with you and your auditors.
We use the NIST AI RMF functions of govern, map, measure and manage to identify and document AI risks and the controls that address them.
For organisations building an AI management system, we structure documentation, roles and lifecycle controls so they map to ISO/IEC 42001 requirements.
We help classify your use case by risk level and build in the transparency, human oversight, logging and data-quality measures the EU AI Act expects.
Data minimisation, purpose limitation, consent-aware pipelines and deletion support, plus providers and settings that do not train on your data.
Access control, encryption, secrets management, logging and secure SDLC practices that support your information-security programme.
For healthcare and payments projects we design around protected health information and cardholder data requirements, including hosting and vendor choices.
Plenty of teams can call an LLM API. Fewer can make an AI feature fast, affordable, secure and maintainable inside a real product. That engineering discipline is what we bring, from startups shipping their first AI feature to enterprises modernising core workflows.
Industry context changes what good AI looks like: the data you have, the rules you follow and the risks you can accept. We bring that context into every AI build. Explore our industry software services:
Choose how you want to work with us. Many clients start with a proof of concept and move to a dedicated team once the approach is proven.
A short, time-boxed build that tests one use case on your real data and ends with results, a recommendation and an estimate for production.
Clear requirements, milestones and budget for a defined AI feature or product, best when scope is well understood.
A long-term team of AI, data and software engineers working as an extension of yours, scaling up or down as priorities change.
Ongoing monitoring, evaluation, retraining and improvement for AI systems already in production, including ones built by others.
From foundation models to vector databases and MLOps tooling, we work across the modern AI stack and choose components your team can run and maintain.
Python
TypeScript / Node.js
PyTorch
TensorFlow / Keras
Scikit-learn / XGBoost
Hugging Face Transformers
OpenAI GPT models
Anthropic Claude
Google Gemini
Llama, Mistral & Qwen
LangChain / LangGraph
pgvector / Pinecone / Qdrant
OpenCV / YOLO
Apache Spark / Airflow
MLflow / Weights & Biases
AWS Bedrock & SageMaker
Azure OpenAI & Azure ML
Google Vertex AI
Docker
Kubernetes
Our AI development process moves in stages, and each stage produces evidence. You see working results early and decide how far to take the build before committing the full budget.
We define the use case, users, success metrics and constraints, and check whether AI is the right tool for the job.
We connect, clean and structure the data the solution needs, set up access rules and build evaluation datasets.
We compare candidate models and approaches on your task, then build a working prototype to validate accuracy and cost.
We develop the full application, train or fine-tune models where needed, and add guardrails, permissions and fallbacks.
We integrate with your systems, run security and load testing, and release to your cloud or private infrastructure.
We monitor quality, drift and cost, collect feedback, retrain and ship improvements on a regular cadence.
Tell us what you want to build. Our AI engineers will review it and reply with questions, a recommended approach and next steps.
Describe the use case, your data and any systems it needs to work with.
Receive the recommended architecture, scope, timeline and cost estimate.
Kick off with discovery or a proof of concept, then scale what works.
AI development services are the engineering work needed to design, build, integrate and maintain AI-powered software. That includes choosing the right models, preparing data, building generative AI applications, AI agents, chatbots or machine learning models, connecting them to your existing systems, and monitoring them in production. For the broader picture, including strategy and business use cases, see our AI services and solutions.
An AI development company turns a business problem into working AI software. A good one will help you pick a use case worth solving, assess your data, build and test a proof of concept, develop the production application with its integrations and security, and support it after launch. It should also tell you when a simpler, non-AI solution is the better choice. If AI is the product you are selling, see our AI product engineering services.
Most AI projects follow six stages: discovery and feasibility, data engineering, prototyping and model selection, full development with training or fine-tuning, integration and deployment, and ongoing MLOps. Each stage ends with something you can review, such as a scope document, test results or a working release, so decisions are based on evidence.
Cost depends on scope rather than on "AI" itself. The biggest drivers are: using an existing model through an API versus training a custom one, the state of your data, the number of integrations, security and compliance requirements, and expected usage, which drives ongoing model and hosting costs. A proof of concept is the lowest-cost way to start; we give a written estimate after a short discovery call.
A focused proof of concept often takes a few weeks. A production-ready first version typically takes a few months, and larger platforms with many integrations or custom model training take longer. Data readiness, integration complexity and approval cycles have the most influence on the timeline.
For most business applications, starting with an existing large language model plus retrieval-augmented generation (RAG) over your data is faster and cheaper. Fine-tuning or training a custom model makes sense when you need a specific style or format, very high accuracy on a narrow task, lower per-request cost at scale, or full control over where the model runs. We benchmark both options on your task before recommending one.
Yes. Much of our AI development work is adding AI features to existing web apps, mobile apps, CRMs, ERPs and internal tools through secure APIs, without rebuilding the product, including AI copilots embedded in your own software. See our dedicated AI integration services and legacy application modernisation.
We design for least-privilege data access, encrypt data in transit and at rest, use providers and settings that do not train on your data, defend against prompt injection, log AI actions and add human review where decisions are consequential. We align the engineering with frameworks such as the NIST AI RMF, ISO/IEC 42001, GDPR and the EU AI Act where they apply to your project. For organisation-wide policies and oversight, see our AI governance consulting services.
You do. Source code, prompts, evaluation datasets, fine-tuned model weights, infrastructure configuration and documentation created for your project are handed over to you. Third-party models accessed through an API remain subject to their provider's terms.
CodeBase Coders provides end-to-end AI development services: AI consulting and proof of concept, generative AI and AI agent development, AI chatbots and voice agents, machine learning, AI integration and MLOps, delivered by one full-stack team. You can also hire a dedicated development team for ongoing AI work. Contact us to discuss your project.
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