AI and data · Generative AI
Generative AI development services, from first use case to production
We help product teams pick the generative AI use cases worth building, prove them on real data, and ship them with the evaluation, guardrails and cost controls that keep them working once real users arrive. Drafting, summarising, structured extraction, search, copilots inside your product and image generation where it actually helps.
Short answer
Generative AI development means building features and products where a model produces text, structured data or images: drafting, summarising, extraction, search and in-product copilots. Innovation Insight takes them from use-case selection to production on OpenAI, Claude, Gemini or open-weight models. Discovery with a working prototype costs $3k to $8k, a production feature $8k to $60k, and a generative AI MVP $25k to $60k, with evaluation sets, guardrails and a cost per call agreed up front.
Reviewed by Zain Khalid Malik, CTO & Co-founder · Updated
Where generative AI earns its place
Most generative AI projects that stall do so because the use case was chosen for the demo, not for the work it removes. We start from the task: who does it today, how long it takes, what a wrong answer costs and how you would know the output is good. If those questions have clear answers, a model can usually help. If they do not, plain software or a better form often does the job for less.
| Use case | What the model does | What decides success | Where we have built it |
|---|---|---|---|
| Drafting | Writes a first version from structured inputs or an interview: reports, letters, product copy, memoir chapters | Tone control, edits stay with the user, cost per draft | Chat Your History: interviews turned into chapters with streaming GPT-4 |
| Summarising | Condenses long threads, calls, tickets or documents into a fixed format | Nothing important dropped, nothing added, consistent length | GovDoc AI: contract summaries with page references |
| Structured extraction | Turns free text and PDFs into JSON fields your system can store and validate | Field-level accuracy, schema validation, review queue for low confidence | GovDoc AI: 50+ data points per contract |
| Search and Q&A | Finds by meaning rather than keywords, answers with citations | Retrieval quality, permission checks, cited sources | Cuts Like A Knife: natural-language music search |
| In-product copilot | Suggests the next step, fills a form, explains a screen, inside your existing app | Narrow scope, actions confirmed by the user, latency under two seconds | Treasure Island AI: a guided AI counsellor in a mobile app |
| Image generation | Creates variants, backgrounds, illustrations or mock-ups from a prompt or a reference | Brand consistency, rights to the output, moderation | Used where it replaces a slow manual step, not as a default |
Generative AI development, LLM integration or an AI app?
These three pages on our site cover overlapping work, so here is how we split it. The difference is mostly about where the project starts and how much product has to be built around the model.
| Service | Starting point | Typical result | Typical budget |
|---|---|---|---|
| Generative AI development (this page) | You know AI could help but have not chosen or proved the use case | A prioritised use case, a prototype on your data, then a production feature or product | $3k to $8k discovery, then $8k to $60k or $25k to $60k |
| LLM integration | The use case is clear and the product already exists | A model added to an existing system: summary button, smart search, extraction step | $8k to $60k |
| AI app development | A new product where the AI does the core job | A new web or mobile app built around the model | $25k to $60k |
If the work involves answering from a large document set, see RAG development; if the model must take actions in other systems, see AI agent development. Our post on agentic AI vs AI agents vs generative AI explains the terms without the marketing.
Choosing the model
We test two or three models on your evaluation set before recommending one, and we keep the model behind a single interface so it can be swapped by configuration. Vendors release and reprice models every few months, and the cheapest model that passes your quality bar is usually the right one. Data residency and the cloud you already use often narrow the choice before quality does.
| Option | Good at | Where it runs | When we pick it |
|---|---|---|---|
| OpenAI GPT models | Broad capability, strong tool calling and structured outputs, image generation in the same API | OpenAI API, Microsoft Azure | General drafting, copilots, extraction with strict JSON schemas |
| Anthropic Claude models | Long documents, careful instruction following, writing and analysis | Anthropic API, AWS Bedrock, Google Vertex AI | Contract and policy work, long summaries, data that must stay in AWS |
| Google Gemini models | Multimodal input (images, video, audio), very large context | Gemini API, Vertex AI | Mixed media inputs, teams already on Google Cloud |
| Open-weight models (Llama, Mistral) | No per-token vendor fee when self-hosted, full control of weights | AWS Bedrock or Vertex AI as managed endpoints, or your own GPUs | Strict residency, very high volume of simple tasks, on-premises needs |
| Small or distilled models | Fast and cheap on narrow tasks | Any of the above | Classification, routing and short extraction at scale |
On GovDoc AI, Claude runs through AWS Bedrock so solicitation documents never leave the client's AWS account. On Chat Your History, GPT-4 and GPT-3.5-turbo sit side by side so the client can compare quality and cost per story.
Guardrails and evaluation
A generative feature without an evaluation set is judged by whoever looked at it last. We build the evaluation set before the feature, and every prompt, model or retrieval change is scored against it.
- Evaluation set: 50 to 200 real inputs with good outputs or scoring rules, written with your domain experts. It grows from every production failure.
- Automated scoring: exact checks for structured fields, rubric scoring by a second model for prose, and a weekly human review of a sample.
- Structured output: JSON schemas enforced by the API, then validated in code, so a malformed answer is rejected rather than stored.
- Grounding: answers only from supplied context, with citations, wherever facts matter. Free generation is reserved for drafting the user will edit.
- Input and output filters: prompt-injection checks on user and document input, PII redaction before text reaches a third-party model, moderation on generated images.
- Human review: a queue for low-confidence or high-stakes outputs, with the reviewer's correction fed back into the evaluation set.
Cost per call and caching
Running cost is tokens in times the input price plus tokens out times the output price, multiplied by calls per user. We model it per feature during discovery, set a budget per user per day in code, and report it from the first week of the pilot. The levers that move it most:
- Prompt caching: a long system prompt or reference document reused across calls can be cached. On Anthropic's API, cache reads cost a tenth of the base input price for most models (checked October 2026).
- Batch processing: work that does not need an instant answer, such as overnight summaries or bulk extraction, goes through batch APIs. Anthropic's Message Batches API is priced at 50% of standard rates, with most batches finishing within an hour (checked October 2026).
- Model routing: easy requests go to a small model, hard ones to a larger model, decided by a cheap classifier or by rules.
- Output length: most cost is in output tokens, so we set length limits and ask for structured fields rather than prose where we can.
- Response caching: identical or near-identical questions are answered from a cache with a short lifetime.
Chat Your History records token usage for every generation, which the client uses to monitor cost and to A/B test prompt templates and models. That habit, logging cost per call from day one, is the single most useful thing we carry from project to project.
From proof of concept to production
- Discovery (one to two weeks, $3k to $8k): rank candidate use cases by value and risk, collect real examples, draft the evaluation set, compare models on a thin prototype and give you a fixed estimate.
- Proof of concept (two to four weeks): the chosen use case working on your data, scored against the evaluation set, with a measured cost per call and latency. The gate: does it beat the current process on quality, time or cost?
- Pilot (four to eight weeks): the feature inside your product for a group of real users, with logging, feedback buttons, guardrails and a review queue. The gate: users keep using it and the error rate is acceptable.
- Production: hardening, permissions, monitoring, cost budgets and documentation. A production feature in an existing product costs $8k to $60k; a new generative AI MVP costs $25k to $60k.
- Support: model upgrades re-qualified against the evaluation set, prompt tuning from real failures and cost reviews, on a retainer of $1.5k to $8k per month.
What generative AI development costs
| Engagement | Cost | Timeline |
|---|---|---|
| Discovery with prototype and fixed estimate | $3k to $8k | 1 to 2 weeks |
| Generative AI feature in an existing product | $8k to $60k | 4 to 12 weeks |
| Generative AI MVP (new product) | $25k to $60k | 10 to 14 weeks |
| Full product with several roles, integrations and admin | $50k to $150k+ | 4 to 8 months |
| Ongoing support, model upgrades and tuning | $1.5k to $8k per month | Monthly |
Model usage is billed by the vendor directly to you and is usually small at pilot volumes. The generative AI development cost guide breaks the build cost down by feature, the AI agent and chatbot cost calculator estimates running cost for your volume, and pricing lists every published rate.
Generative AI we have shipped
- Chat Your History: streaming story generation over Server-Sent Events with GPT-4 and GPT-3.5-turbo, prompt templates stored in the database, and three creativity tiers mapped to model temperatures of 0.5, 1.0 and 1.5. Users edit the generated chapters in a rich-text editor.
- GovDoc AI: structured extraction of 50+ data points from government contracts with Claude on AWS Bedrock, using a three-pass analysis that extracts, audits for missing sections and fills the gaps.
- Treasure Island AI: an OpenAI counsellor persona with a twelve-message context window that generates up to 30 brainstorming questions per idea and feeds a generated business plan report.
- Cuts Like A Knife: natural-language search over a music catalogue with OpenAI and LangChain, combined with audio similarity from embeddings.
If you already know the use case and want it inside your product, start with LLM integration. If AI is the product itself, see AI app development. For the wider AI and data practice, go to AI and data services.
Sources and further reading
Next step
Tell us what you're building and get a written estimate.
A senior engineer replies within one business day. NDA on request.
Products we've shipped, and what happened next.
Case studies written from the technical documentation of each project: the stack, the scale and the outcome.
Questions we get asked a lot.
How much do generative AI development services cost?
Discovery with a working prototype costs $3k to $8k. A production generative AI feature in an existing product costs $8k to $60k, a new generative AI MVP $25k to $60k, and a full product $50k to $150k+. Model usage is billed separately by the vendor.
What is the difference between generative AI development and LLM integration?
LLM integration adds a model to a system you already have, for a use case you have already chosen. Generative AI development starts earlier: choosing and proving the use case, then building the feature or a new product around it.
Which model should we use: OpenAI, Claude, Gemini or an open model?
The cheapest one that passes your evaluation set on quality and latency, within your data residency rules. We test two or three side by side during discovery and keep the model swappable by configuration.
How do you stop generative AI from making things up?
Grounding answers in supplied context with citations, enforcing structured outputs, scoring every change against an evaluation set and sending low-confidence or high-stakes outputs to a human review queue.
How long does a generative AI proof of concept take?
Two to four weeks after a one to two week discovery. You get the use case working on your own data, a score against the evaluation set and a measured cost per call, which is enough to decide whether to pilot.
Is our data used to train the vendor's models?
Not on the paid API terms we use with OpenAI, Anthropic and Google. Where data must stay in your cloud, we run models through AWS Bedrock or Google Vertex AI, or host an open-weight model you control.