AI Product Strategy
Use case selection, model selection, the architecture and the roadmap, before the first commit.
AI Development
AI Application DevelopmentProducts With Intelligence Built In.
AI application development for products that need intelligence at the core — a custom AI app with the model, the data pipeline, the UX and the evaluations built in, not a chat box bolted on. We build AI products that ship to production, not demos that stay in a notebook.
10+Years Experience
100+Projects Delivered
50+Expert Developers
20+Industries Served
AI Product Strategy
Generative AI Apps
Predictive AI Apps
Conversational AI
Computer Vision Apps
Model & Data Ops
Overview
An AI application is a product, not a model call
An AI application is a product that happens to call a model. The work is not the prompt — it is the data, the UX, the evaluation, the guardrails, the cost controls, the observability, the fallback when the model is wrong, the user feedback loop, the model swap path. The model is one component in a system, not the system.
We build AI applications with the model, the data pipeline, the UX, the evaluation suite, the guardrails and the cost controls as part of the architecture from sprint one. The output is a product that ships to production, not a demo that lives in a notebook.
This is the wrong engagement if the goal is a one-off prompt experiment. The investment in an AI product is worth it only when the model call is the product, not a feature.
- Production-Grade — Built to ship, with the evaluations, the guardrails and the observability in place.
- Model-Agnostic — A model swap path, not a single-vendor lock-in. OpenAI, Anthropic, open source, your own.
- Cost-Defensible — Token budgets, caching, batching and the cost review that keeps the bill honest.
- Evaluated, Not Vibes — An evaluation suite that runs on every change, with the regressions caught before deploy.
What we deliver
Everything included in our ai application development
Generative AI Apps
Apps that generate text, image, audio or video against the user prompt and the data.
Predictive AI Apps
Apps that predict, classify or score against the model and the data the product owns.
Conversational AI
Conversational experiences with memory, tool use and the fallback that does not break.
Computer Vision Apps
Vision products that read images, video or documents against the trained model.
Model & Data Ops
Model swap, fine-tuning, evaluation, observability and the operations behind the AI product.
Our process
A proven process for successful delivery
01
Discover
We agree the use case, the data, the model and the architecture on paper.
02
Plan & Design
We design the system, the data pipeline, the UX and the evaluation suite.
03
Develop
We build in two-week sprints with a working slice every Friday.
04
Deploy
We ship to production with the evaluations, the guardrails and the observability live.
05
Optimize & Grow
We read the data, the cost and the evaluations, and ship the next iteration.
Technology
Built with a stack that stays maintainable
Models
- OpenAI
- Anthropic
- Google Gemini
- Open source LLMs
Backend
- Python
- Node.js
- LangChain
- LlamaIndex
Vector & Data
- Pinecone
- Weaviate
- pgvector
- PostgreSQL
Observability
- LangSmith
- Helicone
- OpenTelemetry
- PostHog
What you can expect
- To First Production Release
- 8-14 wksTo First Production Release
- Target Uptime
- 99.9%Target Uptime
- Evaluations on Every Change
- 100%Evaluations on Every Change
- Agnostic Architecture
- ModelAgnostic Architecture
Industries we serve
Delivered for teams in your sector
It depends on the use case, the data, the latency and the cost. We pick against the requirements, not the hype — OpenAI, Anthropic, Google, open source, or a fine-tuned model on your own data. The architecture is model-agnostic, so the next model swap is a configuration change, not a rewrite.
An evaluation suite is part of the architecture from sprint one. The suite includes a held-out test set, a rubric for the qualitative checks, and a CI gate that catches regressions before deploy. Every prompt change, every model swap, every fine-tune is run against the suite, and the result is in the deploy log.
Token budgets, caching, batching, model routing, and a cost review that keeps the bill honest. The cost is a non-functional requirement, not an afterthought. We design against the cost from the first sprint, and the dashboards show the per-request cost against the budget.
Yes. The same architecture runs on cloud, on-premise or hybrid, with the deployment model agreed in the discovery. For sensitive data, the model can run on your VPC or on your hardware, with the same engineering rigour as the rest of the system.
Related services
More in AI Development
Business software
ERP, CRM, HRMS & custom software we build
Ready to start your ai application development project?Let’s scope it together.
Tell us the outcome you need. We’ll come back with an approach, a timeline and a written estimate.