Generative AI development
that survives production.
We build LLM applications, RAG, copilots, agents, and document systems — integrated into the stack you already run, evaluated before launch, and owned by you at handoff.
Generative AI,
scoped to a real task.
Generative AI development at Dwayo covers LLM applications, multimodal workflows, retrieval-augmented generation, copilots, agents, document-processing systems, and model integration — built as one coherent system, not a chain of disconnected tools.
Most engagements start narrow: one workflow, one measurable outcome, evaluated before launch. We expand scope once the first piece is in production and earning its keep, not before.
For a narrower starting point, see AI agent development, AI chatbot development, document AI, or AI copilot development — this page covers the full span those pages each specialize in.
Where this works,
and where it doesn't.
Generative AI is the wrong tool for a lot of problems people bring to it. We'll tell you if yours is one of them.
- The task involves unstructured text, documents, or conversation
- You can define what a correct answer looks like, even roughly
- A human currently does this work and it's expensive to scale
- Partial automation with human review still saves real time
- The task has one deterministic correct answer (use normal software)
- You need 100% accuracy with zero tolerance for edge cases
- The underlying data isn't available or is too messy to use yet
- Nobody can define success beyond "make it feel smart"
Every phase,
with a real deliverable.
What you actually get at each stage of the engagement.
Choosing a model
without getting locked to it.
Hosted frontier models, open-weight models, and small fine-tuned models each win on different axes — capability, cost, data residency, and latency. We pick per task, and build with routing and fallback so the choice isn't permanent. See LLM integration for how provider abstraction works in practice.
A provider abstraction layer means a model swap is a config change, not a rewrite.
Cheaper or smaller models handle the easy cases; expensive calls reserved for what actually needs them.
A provider outage or rate limit degrades gracefully instead of taking the feature down.
| Model class | Best when | Cost profile | Data residency |
|---|---|---|---|
| Hosted frontier (Claude, OpenAI, Gemini) | Task needs strong reasoning, long context, or is still evolving in scope | Highest per-token cost; no infrastructure to run | Depends on provider's data-retention terms — zero-retention agreements where available |
| Open-weight self-hosted (Llama, Mistral via Hugging Face) | Data can't leave your environment, or volume makes per-token pricing expensive at scale | Higher upfront infrastructure cost, lower marginal cost per request | Full control — can run entirely in-VPC or on-premise |
| Fine-tuned small model | A single, narrow, high-volume task where prompting a large model is overkill | Training cost upfront, then cheapest per-request cost of the three | Same as hosting choice — can be self-hosted for full residency |
Most engagements start on a hosted frontier model because it's fastest to validate the use case, then move to routing or a smaller fine-tuned model once volume and the task's actual reasoning requirements are known — not before.
Three phases,
priced per milestone.
We don't quote a single number for "generative AI development" — we quote each phase once its scope is fixed, so you approve spend incrementally instead of signing one large, vague estimate.
| Phase | Duration | What ships | Priced |
|---|---|---|---|
| Discovery & architecture | 1–2 weeks | Written design doc: model choice, data flow, eval plan, human-in-the-loop points | Fixed fee, quoted upfront |
| Build & evaluation | 3–6 weeks | Working system against the eval set, with retrieval/data pipeline live | Fixed milestone, quoted after discovery |
| Integration & handoff | 1–3 weeks | Live in your product, source code and runbook transferred | Fixed milestone, quoted after build |
Controls, not
a policy document.
Every generative AI system we build handles data exposure, retention, and access control as design decisions, not an afterthought.
- Zero-retention model providers used where available
- Access control scoped to what each workflow actually needs to see
- Audit logging on every model call that touches customer or business data
- Output validation before anything reaches a customer or a downstream system
- Human approval gates on any action that's expensive or hard to reverse
Full detail on our Trust & Safety page, including EU AI Act and GDPR considerations for UK and EU teams.
What "done"
actually means.
We agree on these numbers before launch, so acceptance isn't a judgment call after the fact.
The percentage of real inputs the system handles correctly against your test set.
How often an answer is actually supported by the retrieved source, not invented.
Response time under realistic load, not a cherry-picked demo query.
Total inference cost divided by completed tasks — the number that actually matters at scale.
How often the system correctly says "I don't know" instead of guessing.
Two outcomes
from production systems.
reduction in support tickets needing a human, after a WhatsApp AI agent took over routine order-status, product, and sales questions for an e-commerce retailer.
lower per-document inference cost for a fintech underwriting pipeline, through model routing, caching, and context trimming — same extraction accuracy, lower bill.
Both numbers come from before/after measurement against the client's own baseline — support-ticket volume and per-document inference spend, respectively — not a synthetic benchmark. We hold ourselves to the same standard internally: the agent testing & evaluation methodology we use to certify an agent before launch is the same one that produced these figures after launch.
Where generative AI
earns its keep.
| Industry | Common use case | Relevant service |
|---|---|---|
| E-commerce | Order-status and product Q&A over chat/WhatsApp | E-commerce |
| Fintech | Document intake and underwriting support | Document AI |
| SaaS products | Embedded copilots and in-product support | AI Copilot Development |
| Internal operations | Knowledge retrieval over policies and documentation | RAG Development |
What we need
from you.
Representative examples of the task (real documents, real conversations, real edge cases), access to the systems the output needs to reach, and one person on your side who can say yes or no to a design decision quickly. That's the shortlist — the rest is on us.
- A real sample, not a cherry-picked one. 50–100 representative examples including the messy and ambiguous cases, not just the clean ones a demo would use.
- A definition of "correct," even approximate. If nobody can describe what a right answer looks like, that's a scoping problem to solve before the build starts, not during it.
- Access to the destination system. Read (and where needed, write) access to wherever the output actually needs to land — a CRM, a support desk, an internal tool.
- One decision-maker. Someone who can approve a design tradeoff within a day or two, so the project doesn't stall waiting on a committee.
No sales pitch,
just how we work.
Built inside your existing stack — not a rewrite, not a long hiring cycle.
A test set and scoring method exist before launch, not retrofitted after something breaks.
Security, data handling, and compliance commitments on paper, not a verbal promise.
You own the code and the model config at completion. We don't hold it hostage.
Tools we've shipped
in real projects.
Before you
book a call.
How much does generative AI development cost?
We price by milestone. A single LLM feature with retrieval is a different scope from a multi-agent system with document processing underneath it. Book a call and we'll scope your specific case before any commitment.
How long does a generative AI project take?
We aim for a first production feature within six weeks. Larger systems are phased so you see working software early, with evaluation and guardrails built in from the first milestone, not bolted on at the end.
What happens to our data?
We use model providers with zero data-retention agreements where available, encrypt data in transit and at rest, and support VPC, private-cloud, or on-premise deployment when you need it. Full detail on our Trust & Safety page.
Do you build this with us or hand it over?
You own the source code, prompts, and model configuration at project completion. We document the system so your team can operate and extend it without us.
Should we fine-tune a model or use RAG?
Most production cases we see are solved with retrieval, not fine-tuning. We only recommend fine-tuning when retrieval genuinely can't solve the problem — we'll tell you honestly which one your case needs.
What if the model provider changes pricing or deprecates a model?
We build with a provider abstraction layer by default, so a model swap is a configuration change, not a rewrite. See our article on avoiding AI vendor lock-in.
Should we use a hosted model, a self-hosted open-weight model, or fine-tune something small?
Most projects start on a hosted frontier model because it's fastest to validate the use case against real traffic. We move to a self-hosted open-weight model when data residency rules out an external API, or to a fine-tuned small model once a single high-volume task is well understood and prompting a large model is paying for reasoning you don't need.
Tell us what
you're trying to build.
Book a 30-minute call — we'll tell you honestly what it takes to ship it, and what order to build it in.