Generative AI Development
Software built around large language models. Copilots, assistants, and content tools tuned to your domain.
In short: Software built around large language models. Copilots, assistants, and content tools tuned to your domain. LLM integrations tuned to your domain. Content pipelines, copilots, and assistants that speak the language of your product.
Generative AI development is building software around large language models. The work is not calling an API. The work is making the model reliable inside a real product, where it has to be accurate, fast, cheap to run, and hard to embarrass you. We build the retrieval, the prompt layer, the evaluation harness, and the guardrails that turn a general model into a tool that does a specific job in your domain.
Capabilities
RAG pipelines
Retrieval-augmented generation that grounds the model in your documents. The model answers from your data instead of inventing it. We tune chunking, embeddings, and reranking for your content.
Copilots and assistants
In-product assistants that draft, summarise, search, and take action. Designed around the workflow they sit inside, not as a chatbot bolted on the side.
Content pipelines
Systems that generate, transform, and route content at scale. Product descriptions, support replies, reports, and translations, with a person in the loop where it matters.
Fine-tuning
When prompting is not enough, we fine-tune open-source models on your data. Lower cost, lower latency, and tighter control over output than the frontier APIs.
Prompt engineering and evals
Prompt design treated as engineering. Versioned, tested against an evaluation set, and reviewed before a change ships.
Guardrails and safety
Input and output filters, PII scrubbing, topic restrictions, and human-review hooks. The model stays inside the behaviour you signed off on.
Deliverables
- RAG pipeline grounded in your data
- Evaluation harness with golden set
- Prompt library, versioned
- Latency and cost budget
- Guardrails and content filters
- Monitoring and logging
Tools we build with
Models
Framework
Vector store
Backend
How you can work with us
Prototype
2 to 4 weeksA focused proof of concept that proves the hard part of your idea works. Clickable UX, the core feature running end to end, and a link you can share.
Testing the idea with users or investorsMVP
8 to 16 weeksThe smallest product that solves the problem for real users. Built on the same backbone the production system will use, so you do not throw it away when you grow.
Going to market with the first versionFull product
12 to 24 weeksStrategy, design, engineering, the AI work, and launch, run by one team. We take the product from the first whiteboard to production traffic.
Shipping a complete product to customersOngoing retainer
MonthlyAfter launch we stay on. Bug fixes, monitoring, new features, and the work that keeps the product improving once real users are inside it.
Post-launch iteration and supportBuilt with this
Splurge
A fast, focused CRM for small sales teams. Pipeline visibility, deal tracking, follow-ups. No enterprise onboarding tax.
Read case study →HealthTech · AINutriTrac
A nutrition tracker that turns a photo of a meal into a calorie and macro breakdown. The photo is the entire input.
Read case study →Generative AI Development questions
RAG or fine-tuning, which do we need?
Most products need RAG first. It grounds the model in your data and is cheaper to change. Fine-tuning is the right call when you need a specific tone, lower latency, or lower cost per call than the frontier APIs allow. We often end up with both, but RAG comes first.
How do you stop the model from making things up?
Grounding, guardrails, and evaluation. Retrieval grounds answers in your documents so the model has less reason to invent. Output checks flag low-confidence answers. And the eval harness catches regressions before users see them. Hallucination is not solved, but it is managed.
What does an LLM feature cost to run?
It depends on model choice, prompt length, and traffic. A well-designed RAG feature on a frontier model often costs cents per query. We design to a cost budget from day one and track spend in production, so the bill does not surprise you.
Can we run a generative AI feature on our own infrastructure?
Yes. We deploy open-source models like Llama and Mistral on your cloud or on private hardware when data residency, cost, or compliance rules out a hosted API. The architecture is the same. The hosting changes.
AI Development
Custom models, LLM integrations, and AI-native products. We design the model, the interface, and the feedback loop together, because that is where an AI product gets useful or fails.
Explore →AI · StrategyAI Consulting and Strategy
Find the AI use cases worth building, sequence them into a roadmap, and decide on the architecture before any code is written.
Explore →AppMobile App Development
Cross-platform and native mobile apps. React Native and Flutter for one codebase, Swift and Kotlin when a screen needs to go native.
Explore →AppWeb App Development
Production web apps in React, Next.js, Vue, and Nuxt. Dashboards, multi-tenant SaaS, internal tools, and design systems.
Explore →CloudCloud and DevOps
Cloud infrastructure, CI/CD, and observability. Mostly AWS and Google Cloud, with Terraform for the parts you want reproducible.
Explore →StrategyUI and UX Design
Wireframes, prototypes, and design systems. Every screen traces back to a user research finding, not to a hunch.
Explore →Want this for your product?
Tell us what you are building. We will say back whether we can help.
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