LLM application development
Applications built on GPT, Claude, Gemini or open-source models, with structured outputs, tool calling and retrieval so responses are grounded and machine-readable.
AI Services
AI development services engineered for production.
Our AI development services turn a clear business problem into working software: models, APIs and interfaces that hold up under real traffic. We write the code, build the evaluation harness and ship it into your infrastructure with the same rigor as any critical system.
- Full-stack AI engineering
- Evaluation-driven builds
- Your cloud or ours
01 Overview
Custom AI software that survives contact with users
A demo that works on ten hand-picked examples is easy. Software that behaves on the ten-thousandth messy input is the hard part, and it is where our AI development services focus. We treat models as one component of a larger system, surrounded by validation, fallbacks, caching, logging and the guardrails that keep outputs useful and safe.
Custom AI development with us usually starts with a narrow, testable slice of the problem. We assemble a representative dataset, define what good looks like, then iterate on prompts, retrieval, fine-tuning or classical ML until results clear the bar. From there we harden the service, add observability and integrate it with your product.
02 Capabilities
AI Development capabilities.
End-to-end AI Development: strategy, engineering and the production operations that keep it working.
Custom model training
Training and fine-tuning classifiers, rankers and generative models on your proprietary data when a general-purpose model is too slow, too costly or too generic.
AI app development for users
Front-end experiences that make AI features usable: streaming responses, editable suggestions, confidence cues and feedback controls that feed back into quality improvement.
Backend and API engineering
Scalable services in Python or Node.js with queuing, rate limiting, caching and retries, designed so model calls never become the weak link in your stack.
Evaluation and testing
Golden datasets, automated graders and regression tests that catch quality drops before release, turning subjective impressions of model output into numbers you can track.
Deployment and MLOps
Containerized deployments on Kubernetes or managed cloud services, with model versioning, rollbacks and pipelines that make shipping a new model a routine change.
03 How we deliver
From first call to production.
Every AI Development engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Adding AI features to a SaaS product
Summaries, smart search, drafting assistants and auto-tagging built into your existing product, matching its design system and permission model rather than bolted on.
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UC-02
Classifying and routing incoming requests
Models that read emails, forms or tickets and send each one to the right queue with extracted fields, cutting manual sorting for operations teams.
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UC-03
Generating personalized content at scale
Product descriptions, outreach drafts or learning materials generated from structured data, with templates, brand rules and review steps that keep quality consistent.
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UC-04
Building internal tools for analysts
AI software development for analyst-facing tools that query databases in natural language, explain results and export clean reports without waiting on the data team.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Models and APIs
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Llama
- Mistral
Engineering
- Python
- PyTorch
- Hugging Face Transformers
- FastAPI
- Next.js
Deployment
- Docker
- Kubernetes
- AWS SageMaker
- MLflow
- GitHub Actions
06 Industries
Built for your sector.
We deliver AI Development for regulated and fast-moving industries alike.
What is the difference between AI development services and AI consulting?
Consulting answers what to build and why: strategy, feasibility and roadmap. Development is the hands-on engineering that produces working software, including data pipelines, models, APIs, user interfaces and deployment. Many clients start with a short consulting phase to validate the idea, then move straight into development with the same team.
Do you build custom models or use existing ones?
Both, depending on the problem. Foundation models with strong prompting and retrieval cover many needs quickly. When a task is specialized, volume is high or data cannot leave your environment, custom AI development with fine-tuned or self-hosted models often wins on accuracy, latency or cost. We test the options against your data before committing.
How do you measure whether an AI feature is good enough to ship?
We agree on acceptance criteria up front, build an evaluation set that reflects real inputs, and score each iteration automatically. Metrics vary by task, from accuracy and recall to groundedness and format validity. Human review of sampled outputs complements the numbers, and the same checks run in CI to prevent regressions.
Who owns the code and models you build?
Ownership terms are set in the contract, and our standard approach is that the source code, trained model weights and project documentation created for you belong to you. Third-party models accessed through APIs remain subject to their providers' terms, which we review with you during architecture planning.
Can you work on an existing AI codebase?
Yes. We regularly pick up prototypes or production systems built by other teams. We start with a code and architecture review, add evaluation and monitoring if they are missing, then prioritize fixes for reliability, cost or quality before extending the feature set.