AI/ML
Custom generative AI applications — from LLM-powered workflows to fine-tuned production models — built and deployed directly into your product stack.
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What it is
Generative AI refers to large language models and diffusion systems trained to produce new content — text, code, images, and structured data — from learned patterns. These models can be accessed via API, fine-tuned on proprietary data, or deployed privately within your infrastructure.
What you get
Most generative AI projects fail between the proof of concept and production. The gap is not the model — it is the surrounding infrastructure: prompt management, output validation, retrieval pipelines, latency budgets, and the cost of running inference at scale. We build the complete system, not just the API wrapper.
We work with GPT-4o, Claude 3.5, Mistral, Llama 3, and open-source models from Hugging Face. Our team handles model selection based on your latency, cost, and data-privacy constraints — and builds the retrieval-augmented generation (RAG) pipelines or fine-tuning workflows that make outputs reliable rather than probabilistic.
Engagements start with a focused discovery sprint to map your use case to the right architecture, define success metrics, and identify data requirements before a single line of production code is written.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Prompt engineering and output validation
Multi-modal AI — text, image, and structured data
LLM orchestration with LangChain and LlamaIndex
Private model deployment (on-prem or VPC)
Cost and latency optimisation for inference at scale
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
Built with
Generative AI refers to artificial intelligence systems that can create new content including text, images, code, and more. It uses large language models and deep learning to produce human-like outputs.
Custom AI solution costs vary based on complexity, typically ranging from $25,000 for MVPs to $150,000+ for enterprise-grade systems with fine-tuned models and integrations.
A typical AI proof-of-concept takes 4-6 weeks. Full production solutions take 3-6 months depending on data requirements, model complexity, and integration scope.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
