We design and engineer AI-powered products from the ground up — LLM-driven features, AI agents, and intelligent tools built into your product or workflow, not a widget dropped on top of it. From proof of concept to production, with the model, data, and infrastructure choices that actually hold up at scale.
Most agencies that claim "AI development" mean adding a chat widget to your website. We mean something narrower and more useful: engineering software where a language model, retrieval system, or autonomous agent is a first-class part of the architecture — designed, tested, and evaluated like any other production system.
That covers everything from an AI copilot embedded in your SaaS product, to an internal tool that lets your team query company data in plain English, to a standalone AI-powered application built for a specific business problem.
LLM-powered features built into your product, from spec to shipped code.
Connect AI models to your data and tools using the Model Context Protocol.
We benchmark OpenAI, Claude, Gemini, and open-source models against your real data.
Not sure where to start? We assess feasibility before you commit budget.
Six categories of AI systems we design and ship for clients.
In-product AI assistants that help your users get more done inside your existing software.
Retrieval-augmented systems that let AI answer accurately from your own documents and data.
Agents that use tools, call APIs, and complete multi-step tasks with minimal supervision.
Internal tools that let non-technical teams query data, generate reports, or draft content.
Computer vision and document intelligence for extraction, classification, and QA.
Models that forecast demand, score risk, or recommend the next best action.
We choose the stack based on your requirements, not the other way around.
OpenAI GPT, Anthropic Claude, Google Gemini, Llama, Mistral
LangChain, LlamaIndex, MCP SDKs, Semantic Kernel
Python/FastAPI, Node.js, Laravel, tRPC
Postgres/pgvector, Pinecone, Supabase, Redis
AWS, GCP, Docker, Kubernetes, Vercel
LangSmith, Promptfoo, custom eval harnesses
OAuth2, PII redaction, private VPC deployment
Token/cost tracking, latency and quality dashboards
A structured process that de-risks AI investment before you scale spend.
Define the use case, assess data readiness, and confirm AI is actually the right tool. 1–2 weeks.
Choose models, data pipeline, and integration points. 1 week.
A working prototype against real data, so you validate before scaling investment. 2–4 weeks.
Full build and integration with your existing systems. 4-12 minggu depending on scope.
Systematic evaluation against real scenarios, not just spot-checking outputs. 1–2 weeks.
Phased rollout with cost, latency, and quality monitoring. Ongoing.
Every engagement is scoped individually. These ranges reflect the shape of projects we typically deliver — use them for budgeting, not as a fixed quote.
2–4 weeks · validate one use case against real data before committing further budget.
6–10 weeks · one AI feature, fully integrated and production-ready.
3–6 months · multiple features, custom infrastructure, ongoing iteration.
Representative project types, shown to illustrate scope and approach.
We treat AI features like any other production code: version-controlled, tested, and monitored — not a one-off prompt in a notebook.
We're not tied to one model vendor. We recommend what fits your data, budget, and latency needs, and build so you can switch later.
We push for a scoped proof of concept before a large build, so you know it works before committing full budget.
One team handles the model layer, the backend, and the product UI — no hand-offs between separate vendors.
Data minimization, access controls, and deployment options that respect your compliance requirements from day one.
If AI isn't the right tool for your problem, we'll say so before you spend a dollar building it.
AI automation (our other service) takes your existing manual processes and automates them with AI. AI development is broader: we design and build AI-native software from scratch — a product feature, an internal tool, or a standalone application where AI is the core of what you're building, not an add-on to an existing workflow.
It depends on your use case, budget, latency requirements, and data residency needs. We run model evaluations against your actual use case and data before recommending a provider, and we build with an abstraction layer so switching providers later doesn't mean rebuilding your application.
Most business use cases are best served by foundation model APIs (OpenAI, Claude, Gemini) combined with your own data via retrieval, fine-tuning, or prompt engineering — this is faster and cheaper than training from scratch. We only recommend custom model training when the use case genuinely requires it.
The Model Context Protocol (MCP) is a standard that lets AI models securely call tools and access data sources — your CRM, database, or internal APIs — without custom one-off integration code for every model. We build MCP servers and clients so your AI features can plug into new tools and models without a rebuild each time.
We scope every AI project individually based on complexity, data readiness, and integration depth. As a planning guide: proof-of-concept projects typically run $3,000–$8,000, a production AI feature $8,000–$25,000, and a full AI product or platform from $25,000 upward. We provide a fixed-scope quote after discovery.
We work under NDA as standard, minimize what data is sent to third-party model providers, support on-premise or private-cloud deployment where required, and can architect around data residency and compliance requirements relevant to your industry and jurisdiction.
Book a free technical consultation. We'll tell you honestly whether AI is the right fit for your problem, and what a realistic scope and timeline looks like.
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