GenAI Use Case Discovery & Scoping
We identify where generative AI can create content, answer questions or speed up work, and define clear success criteria for each.
- Use-case brief
- Success metrics
AI, Machine Learning & Intelligent Automation
Generative AI Development Services
We design and ship GenAI products for content, code assistance, enterprise search and workflow automation, using leading foundation models with evaluation, guardrails and cost controls built in.
- Content generation
- Code assistants
- Semantic search
- Guardrails & evals
- Years of delivery
- 8+
- Years of delivery
- Projects shipped
- 350+
- Projects shipped
- Clients across US, UK, AU & SG
- 200+
- Clients across US, UK, AU & SG
What’s included
Everything You Need to Launch a Generative AI Product
From use-case design to prompt engineering, safe deployment and cost control, we ship GenAI features your users actually rely on.
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Foundation Model Selection & Architecture
We compare GPT, Claude, Gemini and open-source models on quality, latency, privacy and cost, then design the solution architecture.
- Model comparison
- Architecture diagram
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Prompt Engineering & Evaluation Suites
We craft and test prompts against your real scenarios, so outputs stay accurate, on-brand and consistent as models change.
- Prompt library
- Evaluation dataset
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Content & Code Generation Features
We build generation tools for marketing copy, reports, summaries or code that save your team hours of repetitive writing.
- Generation workflows
- Editor interface
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Semantic Search & Knowledge Assistants
Your staff and customers ask questions in plain language and get answers drawn from your own documents and data.
- Vector search
- Q&A assistant
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GenAI API Integration With Your Stack
We connect generative features to your apps, CRM and databases through secure APIs, so they fit into daily workflows.
- API endpoints
- SSO & access setup
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Guardrails, Safety & Data Privacy Controls
We add content filters, PII redaction and human review steps so your product avoids harmful, leaked or fabricated output.
- Guardrail rules
- Red-team report
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LLMOps Monitoring & Cost Optimisation
We track quality, latency and token spend in production and tune prompts and models to keep running costs predictable.
- Usage dashboard
- Cost reports
Need something specific?
Why Kramate
Why Business Owners Choose Kramate for Generative AI
We build AI writing and answer tools that your team can trust.
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Tools That Do Not Make Things Up
Instead of
tools that sound sure but are wrong
With Kramate
We check its answers against real cases from your business before customers see them.
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Running Costs You Can Predict
Instead of: a monthly AI bill that keeps growing
With Kramate: We show what each use will cost and set limits so spending never runs wild.
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Your Private Data Stays Private
Instead of: worrying where your files end up
With Kramate: We agree in writing which information the AI may see and which it never touches.
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Something You Can Try in Weeks
Instead of: waiting months to see anything
With Kramate: You get a working first version fast, so your team can react to the real thing.
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We Keep Pace as AI Changes
Instead of: tools that go stale in a year
With Kramate: When better AI arrives, we tell you and swap it in without a rebuild.
Our approach
How We Build Your Generative AI Product, Step by Step
You see real generated results early, then we tighten quality and safety before your users do.
6 clear steps 6 deliverables you keep
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Content Goals Workshop
We agree what your tool should write, summarise or answer, who uses it, and what a good result looks like.
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AI Model Selection
We test a few language models on your own sample tasks and compare quality, speed, privacy and cost.
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Prompt Prototype
We write and refine the instructions that steer the model, then show a working draft built on your real material.
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Output Quality Testing
We run hundreds of test questions, mark wrong or off-brand answers, and fix the causes before anyone outside sees them.
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Safety Rules Setup
We add filters, privacy shields and human checks, so the tool avoids harmful, leaked or made-up answers.
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Launch & Cost Watch
We release the feature to your users, then watch usage and spend so running costs stay predictable.
Tools & technologies
Technologies We Use for Generative AI Development
We pick models and tooling around your content, cost and privacy needs.
- OpenAI: Provides GPT language models for text, code and reasoning features.
- Claude: Anthropic language models for careful reasoning and long documents.
- Google Gemini: Google models that handle text, images and long context.
- Mistral AI: Efficient open and hosted language models for cost-aware builds.
- LangChain: Connects language models to your data, tools and workflows.
- LlamaIndex: Indexes your documents so language models can answer from them.
- Hugging Face: Hub of open models and libraries for language and vision work.
- Pinecone: Managed vector database for semantic search.
- Qdrant: Vector database for fast similarity search.
- Elasticsearch: Search engine for text, filters and hybrid retrieval.
- PostgreSQL: Dependable database for complex business data.
- Python: Readable language for business logic, data and automation.
- FastAPI: Python framework for fast, well-documented APIs.
- Next.js: React framework for fast, search-friendly websites and apps.
- React: Component-based user interfaces for web apps.
- TypeScript: Typed JavaScript that catches mistakes before release.
- Docker: Packages software so it runs the same everywhere.
- AWS: Amazon cloud hosting and services.
- Azure: Microsoft cloud hosting and services.
Expert guide
Generative AI Development Services: A Complete Guide for Businesses
Generative AI development services design and build software that produces text, images, code or structured answers using large models, then connect that software to your own content and systems. The work is mostly engineering around the model: retrieving the right information, controlling what the model may do, checking its output and keeping costs predictable.
A first demo is quick. A dependable feature is not, because generated output can be fluent and still wrong. That gap between demo and production is where generative AI development services earn their fee.
This guide walks through model choices, a build pipeline, evaluation, security, running costs and regional rules for the USA, UK, Australia and Singapore, then explains how to select a team and how Kramate works.
What generative AI development services build
Typical deliverables fall into a handful of patterns, each with different risks.
- Assistants over company knowledge: staff or customers ask questions and receive answers with references.
- Drafting tools: first drafts of emails, proposals, reports or product descriptions that a person edits.
- Extraction and summarization: turning long contracts, tickets or call transcripts into structured fields or short briefs.
- Code and content helpers: inside developer or marketing workflows.
- Multimodal features: describing images, reading scanned forms or generating visual variations.
Not every language task needs a large model. Some are handled better by classic NLP techniques that are cheaper and more predictable.
Choosing a model: hosted API or open weights
The model decision shapes cost, privacy and control. Hosted models are called over an API, and open-weight models can be run in your own environment.
| Factor | Hosted API | Open weights you run |
|---|---|---|
| Setup effort | Low | Higher: hosting, scaling and updates are yours |
| Data location | Governed by the provider’s terms and region options | Stays in your environment |
| Quality ceiling | Often strongest on broad tasks | Can match on narrow tasks, especially when adapted |
| Cost shape | Pay per use | Infrastructure plus engineering time |
| Switching | Needs an abstraction layer | Full control over the version |
Provider documentation such as the OpenAI API documentation (opens in a new tab), Claude documentation (opens in a new tab) and Meta’s Llama resources (opens in a new tab) describe capabilities, limits and usage terms. Hugging Face documentation (opens in a new tab) covers many open models. Build so that a model can be swapped, because the best option changes, and connect the feature to existing tools through AI integration.
How a generative AI application is built
- Define the task and the failure modes. What is a good answer, and what must never happen?
- Prepare content. Clean, permission-checked documents are the base of any grounded assistant.
- Ground the model. Retrieval-augmented generation fetches relevant passages at question time, so answers come from your sources. Our RAG and fine-tuning service covers this in depth.
- Design prompts and guardrails. Instructions, output formats and refusal rules.
- Wrap in a real product. Login, permissions, logging, feedback buttons and fallbacks.
- Evaluate, launch in stages and monitor.
Evaluation: how to test output you cannot predict
Traditional tests compare an exact result. Generative output varies, so teams build an evaluation set: a collection of real questions with reference answers or scoring rules, drawn from your own material. Each change to a prompt, model or document set is re-run against it.
Useful checks include whether answers are supported by retrieved sources, whether the system declines when it lacks information, and whether outputs follow the required format. Automated scoring by another model can help at scale, but sampled human review remains necessary. No partner can responsibly promise an accuracy figure before testing on your data.
Security, privacy and responsible use
The OWASP Top 10 for LLM Applications (opens in a new tab) lists risks specific to these systems, including prompt injection, insecure handling of model output and sensitive information disclosure. Prompt injection means hostile text, hidden in a web page or document, tries to override the system’s instructions.
- Enforce access rights at retrieval time, so users only see content they could already open.
- Treat model output as untrusted input before it touches databases or other systems.
- Keep secrets and personal data out of prompts unless needed, and log carefully.
- Check each provider’s retention and training terms before sending real data.
- Add content filters and an escalation route to a person.
The NIST AI Risk Management Framework (opens in a new tab) and vendor guidance such as Microsoft’s responsible AI resources (opens in a new tab) help structure reviews of fairness, transparency and accountability.
Running costs and how to budget
Build cost for generative AI development services depends on integrations, content preparation, evaluation effort and security needs. Run cost is different: hosted models usually bill by tokens, the small units of text processed in each request and response. Long documents, long conversations and frequent calls all increase usage.
- Route simple requests to smaller models and reserve large ones for hard tasks.
- Cache repeated answers and trim context sent with each call.
- Set usage limits per user and alerts on spend.
- Budget for ongoing evaluation, prompt maintenance and model upgrades.
Common commercial models are a fixed price for a defined first release, a dedicated team for continuing product work, and hourly support for experiments.
Industry use cases
- Legal and compliance: a tool that highlights unusual clauses in contracts for a lawyer to review.
- Customer support: an agent-assist panel that drafts replies from past tickets and policy pages.
- Healthcare administration: summaries of long administrative correspondence, with clinical decisions left to clinicians.
- Ecommerce: product descriptions generated from structured catalog data and approved by a merchandiser.
These are illustrative examples, not Kramate results.
Regional considerations: USA, UK, Australia and Singapore
- USA: sector rules (for example in health and finance), state privacy and automated-decision laws, and consumer-protection enforcement all apply to AI features.
- UK: UK GDPR and the ICO’s AI and data protection guidance (opens in a new tab) cover lawful basis, transparency and data minimization.
- Australia: the Privacy Act 1988 and Australian Privacy Principles govern personal information sent to models, including overseas providers.
- Singapore: the Personal Data Protection Act applies, and national agencies have published AI governance guidance.
Ask where model providers process data and whether regional hosting is available, then confirm obligations with legal counsel. If you serve EU residents, the EU AI Act resource (opens in a new tab) tracks that law’s obligations.
Choosing among generative AI development services providers
- Ask to see how they build and run an evaluation set.
- Ask how they defend against prompt injection and data leakage.
- Check that the design lets you change model providers.
- Request a plan for monitoring cost and quality after launch.
- Confirm you own prompts, code, evaluation data and documentation.
How Kramate delivers generative AI projects
After a free consultation you receive a detailed estimate within 24 hours, under an NDA signed before detailed discussions. Work runs in two-week rounds, each closing with a demo of working software, and a named project manager shares daily Slack or Teams updates. Contracts can be fixed-price, dedicated-team or hourly, and the client receives full ownership of all intellectual property. See our delivery approach for the stages. Teams practice least-privilege access, encrypted devices and peer review of all code.
Frequently asked questions
How do I build a generative AI application for my business?
Define the task and what counts as a good answer, prepare permission-checked content, ground the model in it, add guardrails, wrap it in a real product with logging, then test against an evaluation set and launch in stages.
Should we use an open-source or proprietary LLM?
Proprietary hosted models are quicker to start and often strong on broad tasks. Open-weight models give more control over data location and cost at scale but need engineering to host. Many teams design so either can be used.
How do you reduce LLM hallucinations?
Ground answers in retrieved sources, show citations, restrict the scope, instruct the model to decline when unsure, test on real questions and keep human review for important outputs. Errors can be reduced but not removed.
What is prompt injection?
It is an attack where text in a document, email or web page contains instructions meant to hijack the model. Defenses include separating trusted instructions from untrusted content, limiting tool permissions and validating outputs.
Is generative AI safe to use with company data?
It can be, if you choose providers whose terms fit your data, restrict what is sent, enforce user permissions and log access. Review each vendor’s retention and training policies, and involve security and legal teams early.
Why do generative AI costs vary month to month?
Hosted models charge by usage, so longer prompts, bigger documents and busier periods raise bills. Caching repeated answers, using smaller models for easy tasks and setting spend alerts all keep monthly costs predictable and easy to forecast.
Sources and further reading
- OpenAI API documentation (opens in a new tab)OpenAI
- Claude documentation (opens in a new tab)Anthropic
- Llama (opens in a new tab)Meta
- Hugging Face Documentation (opens in a new tab)Hugging Face
- OWASP Top 10 for Large Language Model Applications (opens in a new tab)OWASP
- AI Risk Management Framework (opens in a new tab)NIST
- Microsoft Responsible AI (opens in a new tab)Microsoft
- Guidance on AI and data protection (opens in a new tab)ICO UK
- EU AI Act (opens in a new tab)Future of Life Institute
FAQ
Frequently asked questions
Main factors are the type of output (text, images, code), the foundation model used, how much of your data it must draw on, usage volume, safety filters, interface work and evaluation effort. Per-request model fees continue after launch, so we plan those with you.
Work usually moves from use-case selection to a prototype, prompt and workflow refinement, evaluation, safety checks and staged release. Timelines differ based on output quality demands, number of data sources, review steps needed and how fast your team gives feedback on early results.
You own the application code, prompts, workflows and evaluation sets we deliver, as Kramate transfers IP to clients after signing an NDA before detailed talks. Ownership of AI-generated content can depend on local law and model terms, so we flag those points early.
Yes. We add generation features through APIs into your existing app, content tools or internal workflows. Users stay in familiar screens, with drafts, summaries or answers appearing where they work, and with human review steps where your process needs them.
Compare models on your own sample tasks, not general rankings. Weigh output quality, speed, cost per request, data handling terms, and language support. We build the app so the model can be swapped later, which limits lock-in as the market changes.
We ground answers in your trusted documents, constrain the task, ask the model to cite sources, and add checks before output reaches users. We also test with tricky questions and keep a human review step for high-stakes content. Risk falls but never disappears entirely.
Common mistakes include starting with a tool instead of a problem, skipping evaluation, feeding messy documents to the model, ignoring data privacy, and underestimating running costs. We address these early with clear success measures, test sets and a plan for monitoring after launch.
Taking new projects
Let’s talk about your Generative AI Development Services needs
Tell us about your project and get a free consultation and estimate within 24 hours.
- We reply within 4 business hours A real consultant reads your brief — no bot, no queue.
- 30-minute discovery call We dig into goals, constraints and budget. NDA signed on request.
- Proposal in 24 hours Scope, timeline, team and price — clear enough to decide on.
NDA on request You own the IP No obligation Free estimate