Built a product with an AI feature added on, but the AI is slow, unreliable, or too expensive to run at scale?
AI prototype that works in the lab but falls apart under production conditions?
Building a product that has AI in it is harder than adding an AI feature to a product. The architecture decisions are different. The data pipeline is a product dependency. The model behavior is part of the user experience. The evaluation loop is part of the development process.
We engineer AI-native products from the ground up -- designing the system so that AI is load-bearing from sprint one, not wrapped around a conventional product after the fact.
AI-native architecture designed for the model to be a first-class product dependency
Data pipelines, evaluation loops, and monitoring built into the product from day one
Working AI product shipped in 12--16 weeks -- not a demo that can't handle production traffic
Fixed project cost agreed before development starts
Conventional software products are deterministic. Given the same input, they produce the same output. Testing is straightforward. Debugging is tractable. Latency is mostly a function of infrastructure.
AI-native products are probabilistic. The model might produce different outputs for the same input. Quality degrades when the input distribution shifts from the training distribution. Latency depends on model size and the length of the context window. Cost scales with usage in ways that matter at product economics level.
These properties require different engineering decisions at every layer: the data architecture, the evaluation strategy, the caching and latency management, and the user experience design around uncertainty. Getting these decisions right at the start is much cheaper than retrofitting them after the product is in production. PSi's live voice AI decision platform -- 300+ concurrent users, 75% faster decision-making, 98% cost reduction vs. traditional methods -- was built AI-native from sprint one and launched in 14 weeks. Perceptional's AI research chatbot replaced traditional surveys with 4x deeper insights and 48-hour time to findings.
System architecture where the AI capability is a first-class dependency -- not a feature you call from the application layer. Includes the data model for AI inputs and outputs, the latency budget, the caching strategy, the evaluation infrastructure, and the fallback behaviour when the model is unavailable or uncertain.
The data infrastructure that feeds the AI -- ingestion pipelines from your data sources, cleaning and transformation, embedding generation, vector storage, and retrieval optimisation. Built as production-grade infrastructure, not a notebook you run manually.
Automated evaluation pipelines that run your test suite on every model or prompt change. Quality dashboards that surface accuracy and reliability metrics in production. Feedback collection from users that flows back into the improvement cycle.
Designing the product experience around the properties of AI output -- communicating uncertainty, handling generation time, presenting citations, and designing correction flows when the model is wrong. The UX of an AI product is a distinct engineering discipline.
Engineering the product to hit your cost-per-query and latency budgets -- through prompt optimisation, caching, model selection, batching, and progressive loading patterns. The economics of running an AI product at scale are a first-class engineering concern, not an afterthought.
Instrumenting the product to capture quality signals from production -- which outputs users accept, edit, or reject, and which queries the model handles poorly. Building the feedback loop that improves quality over time without requiring a manual retraining cycle for every fix.
Tell us the core AI capability and the product around it. We'll design the architecture and give you a fixed cost.
Generative AI Development -- LLM applications and content automation
RAG Development -- Retrieval systems for grounding AI in your data
AI Agent Development -- Autonomous agents for workflow automation
Product Engineering -- Business-first product engineering for non-AI products
AI Consulting -- Assessing where AI creates value before committing to build
Tell us the AI capability you need. We'll design the system and give you a fixed cost.
Frequently asked questions
Adding an AI feature treats the model as a black box you call from your existing code. AI product engineering designs the entire product around the AI capability -- the data model, the latency requirements, the evaluation criteria, the feedback loop, and the user experience of AI-generated output. When the AI is core to the product's value proposition, this design-first approach is the difference between a product that works reliably at scale and one that works in demos.
We engineer AI-native products across categories -- B2B SaaS products with AI copilots, consumer apps where AI is the primary interaction layer, internal enterprise tools with AI-powered automation, and platform products that expose AI capabilities to third parties via API. In each case, the engineering challenge is making the AI reliable, cost-efficient, and accurate enough to trust in production.
We build evaluation into the development process from the start. Before launch, we define the quality bar -- accuracy targets, latency budgets, cost per query -- and build automated evaluation pipelines that run against your test cases on every code change. This catches regressions before they reach users. Post-launch, we instrument the product to surface quality signals from production data so the product improves over time.
A focused AI product with a single core AI capability typically takes 12--16 weeks from kickoff to production launch. A full AI platform with multiple capabilities, a management interface, and enterprise integrations takes 5--9 months. We build in 2-week sprints so you see working software throughout.
A focused AI product with one core capability typically runs $40,000--$100,000. A full AI platform with multiple capabilities and enterprise integrations typically runs $100,000--$300,000+. Cost depends on model complexity, data pipeline requirements, and the scale of the user interface. We scope every project before pricing it.
You own everything -- the codebase, the trained models, the data pipelines, and the deployment configuration. We don't retain IP and we don't build on proprietary frameworks that lock you in.