Custom AI Development
Bespoke AI products built ground-up — not API wrappers. Fine-tunes, RAG systems, multi-agent pipelines, and the evaluation harness to keep them honest.
Custom AI development at Sitio Labs means building AI products that are specific to your domain, not off-the-shelf integrations of a public model. We start with the eval set — the inputs and the desired outputs — and design backwards. Models, retrieval, agents, and orchestration are picked to fit the eval, not the press release.
What custom AI development means at Sitio Labs
Custom AI development is the work of building AI products that solve a specific problem your team has — not generic wrappers around ChatGPT. Sometimes it means fine-tuning a model on proprietary data. Sometimes it means a multi-agent pipeline with a planner, three workers, and a critic. Sometimes it means a small Llama-3 model running on your own infrastructure because the data cannot leave your VPC. Custom AI is custom because the constraint set is custom — latency, cost, accuracy, residency, explainability — and we engineer to those constraints, not around them.
How we approach custom AI projects
Eval-first. Before the first line of inference code, we sit with you and define the inputs, the desired outputs, and the metric that says “this is working.” Then we benchmark three candidate architectures — typically a frontier model with prompting, a frontier model with retrieval, and a smaller fine-tune — against that eval. We pick the one that meets the eval at the lowest cost-per-call, instrument it with structured logging and per-input metrics, and build a CI gate that blocks regressions. The deliverable is the system plus the harness; the second is what keeps the first honest a year from now.
When custom AI is worth it (and when it isn’t)
Worth it when: your data gives a meaningful accuracy lift over the public model (you have ≥5,000 high-quality examples), latency requirements rule out frontier APIs, residency rules require on-prem, or the cost-per-call at scale makes a smaller model cheaper. Not worth it when: the public model already meets your accuracy bar, your data volume is small, or the engineering team to maintain it does not exist. We will tell you in discovery which side of the line you are on. The honest answer is sometimes “use Claude with a good prompt and call it done.”
What you get at the end of a Sitio Labs custom AI engagement
A working AI system in your stack, with the evaluation harness it was built against, the prompt and model versioning history, the cost-per-call dashboard, and a runbook your team can use to retrain or upgrade the model. Code is in a Git repo on your account. Models are either hosted by you (we provide the deployment scripts) or via your chosen API provider with the keys in your name. We do not lock you into a Sitio-hosted black box. If the engagement ends, you keep the system.
- ·Domain-specific evaluation harness (the single most undervalued AI deliverable)
- ·Custom fine-tuning or LoRA adapters where the data justifies it
- ·Retrieval-augmented generation over proprietary corpora
- ·Multi-agent orchestration (planner / worker / critic patterns)
- ·Inference cost and latency budgets enforced in CI
- ·Model and prompt versioning, with rollback
From₹8,00,000Scoped per project. Final fee in the SOW.
Timeline4–8 weeks
What changes the price- Number of artifacts in the SOW
- Speed (rush deliveries cost 25% more)
- Number of stakeholders involved
- Whether the brief is signed or still being shaped
What is the difference between custom AI development and AI integration?
AI integration adds an off-the-shelf model (Claude, GPT) to an existing product flow — fast, low-risk, vendor-locked. Custom AI development builds for the parts where off-the-shelf is too expensive, too slow, too generic, or non-compliant — fine-tunes, smaller self-hosted models, multi-agent pipelines.
Do you fine-tune models or just use APIs?
Both, depending on the eval. We fine-tune (full or LoRA) when the data justifies it. We use APIs when the public model already passes eval. The starting question is always cost-per-call at the accuracy you need.
Which models do you work with?
Frontier: Anthropic Claude, OpenAI GPT-4o, Google Gemini. Open-source: Llama 3, Mistral, Qwen, served via Replicate, Together, or self-hosted on your infrastructure. The choice depends on the cost-latency-accuracy-residency constraint set.
Can you deploy AI on our own infrastructure?
Yes. Self-hosted inference on AWS, GCP, or your own GPU servers. Common when data cannot leave the VPC or when scale makes self-hosting cheaper than per-token API pricing.
How long does a custom AI development engagement take?
A typical engagement is 4–8 weeks: 1 week discovery and eval design, 2–3 weeks architecture benchmark and selection, 2–3 weeks build and instrumentation, 1 week handoff. Smaller scopes (a single fine-tune) are 2–3 weeks; larger multi-agent systems are 8–12.
Discuss this engagement.
Discovery is free. We will write you a brief, even if you do not engage us.
Book a discovery call ↗