Draftly: AI LinkedIn content tool
- 500+
- active users in 60 days
- 3x
- increase in posting frequency
Generative AI Development Company
Generative AI development company for AI products users trust.
RaftLabs is a generative AI development company shipping production software since 2015. We build LLM apps, RAG pipelines, and AI agents for clients including Vodafone, Cisco, T-Mobile, Energia, and Nike. A first AI feature starts around $40K to $80K and launches a validated v1 in about 12 weeks; multi-feature builds grow from there. You know the cost before we write a line of code.
Most businesses have bought AI tools. Few have shipped AI that their users actually trust. Off-the-shelf models give you average output, trained on average data, built for average use cases. If your product needs to generate content, process documents, handle customer queries, or automate workflows using your domain knowledge, you need custom development.
We build generative AI software: LLM-powered applications, RAG pipelines, fine-tuned models, AI agents, and content automation systems. Built around your data, your workflows, and your accuracy requirements. Not a generic template.
Custom LLM apps, RAG pipelines, and AI agents (not GPT wrappers)
Working prototype in 2 to 4 weeks before full commitment
Fixed project cost: you know the price before we start
4.9/5 on Clutch, shipping production AI software since 2015
500+ users in 60 days Content generation35% more bookings Image generation12 weeks, concept to launch Conversational AI
The problem
Sound familiar?
Paying for AI tools your team uses for 10% of what they cost?
Built a GPT wrapper but getting generic outputs that don't fit your product?
Worried the output will sound like ChatGPT wrote it, not like you?
Short answer
RaftLabs builds custom LLM apps, RAG pipelines, fine-tuned models, and AI agents for businesses across the US, UK, Europe, Canada, and the UAE. Starter builds cost $40K to $80K and ship a validated v1 in 6 to 8 weeks. Shipping production software since 2015, for clients including Vodafone, Cisco, and T-Mobile. Fixed-price quotes before development starts.
Key takeaways
- Most projects deliver in 12 weeks at a fixed cost of $50,000 to $150,000.
- A working prototype takes 2 to 4 weeks. You can validate the approach before committing to the full build.
- 78% of IT leaders have been hit by unexpected AI consumption charges, and only 43% have formal AI governance in place (Zylo, 2026). We estimate your run-rate before you commit and you pay model bills directly, at cost.
- You own everything at project end: code, model weights, data pipelines, and infrastructure.
- Clients include Vodafone, Cisco, T-Mobile, Energia, and Nike. Shipping production software since 2015.
Trusted by
The output that sounds like it came from inside your business, not the average of the internet.
Picture the version you actually want. A customer reads what the AI generated and can't tell it wasn't written by your best person. It's grounded in your documents, your tone, your edge cases. Nobody on your team is quietly proofreading every output before it goes out the door.
Now picture the version most teams ship instead. A demo that wowed the room, built by wiring up an API over a weekend. Then real customers show up, and it either reads exactly like ChatGPT, generic enough that a competitor's identical wrapper could have written it, or it hallucinates something confident and wrong, and a customer catches it before your team does.
Most companies have tried AI tools. Few have shipped AI products their users trust.
What is generative AI development
Generative AI development is building software around a language or image model so its output, text, code, images, structured data, is grounded in your own data and brand, not the average of the public internet. It covers custom LLM applications, RAG pipelines that ground answers in your documents, fine-tuned models, and the evaluation and guardrails that keep output reliable once real customers are using it. That's the difference from AI integration: integration adds an API call to software you already have; development builds the product, the retrieval pipeline, and the evaluation harness underneath it, from scratch, around your problem.
Generative AI moved from pilot to standard fast: Gartner has projected that by 2026 more than 80% of enterprises will have used generative AI APIs or deployed generative AI applications, up from less than 5% in 2023, which is why the hard part is no longer the demo but shipping something production-grade.
RaftLabs has shipped generative AI products across SaaS, service businesses, healthcare, and professional services, from Draftly's AI LinkedIn drafting tool (500+ active users in 60 days) to Makeover.ai's photorealistic image generation (35% more bookings) to Perceptional's conversational research platform, shipped in 12 weeks. The AI layer is one part of a complete product: authenticated, logged, monitored, and maintainable.
Why most generative AI never earns anyone's trust
The odds today
- 66%
- of marketers say AI-generated content reads as thin or generic, their top complaint about it
- Brafton, 2026 marketer survey
- 55%
- of consumers view a brand less favorably once they can tell content is AI-generated
- Brafton, 2026 marketer survey
- 78%
- of IT leaders have been hit by unexpected AI consumption charges; only 43% have formal governance
- Zylo, 2026 SaaS Management Index
The failure isn't the model. It's shipping generated output without checking it against anything. Starting in November 2022, CNET quietly published 77 AI-generated financial explainer articles under a staff byline. After an outside investigation, CNET's own editorial review found factual errors in 41 of the 77 articles, including a compound-interest miscalculation, and paused the program. Nobody at CNET wanted wrong financial advice published under their name. The gap was that nothing evaluated the output against a real standard before it went live, the exact step a "GPT wrapper" skips and a real build doesn't.
What you've probably already tried, and why it stalled
- Wired up the API yourselves
- A weekend prototype using the model API directly. Impressive in a demo, then it hits messy real inputs: no retrieval grounding, no evaluation, and the first hallucinated answer a customer sees is a trust event, not a bug ticket.
- Hired an "AI agency" that turned out to be a wrapper shop
- Paid real money for what turned out to be a system prompt and a UI. The tell: they can't produce an architecture diagram or an evaluation result, and the senior person who sold the deal isn't the one who built it.
- Adopted an off-the-shelf AI tool
- Fine while the workflow is generic. The moment your product's actual differentiation depends on domain-specific output, your documents, your tone, your edge cases, the tool's generic training data shows, and the feature reads as bolted-on, not native.
- Built it in-house with your existing team
- Your engineers can call an API. Few teams have shipped a RAG pipeline or an evaluation harness at production volume before, so that engineering discipline gets skipped or bolted on late, and the token bill or the hallucination rate becomes a fire drill after launch, not a decision made on purpose in week one.
How we close the gap
RAG, fine-tuning, and an evaluation harness are the engineering discipline that separates a real build from a wrapper, and we treat them as the default, not an upsell. Every engagement starts with a working prototype in 2 to 4 weeks, tested against your real inputs, so you validate the approach before committing to the full build. What ships afterward is grounded in your data, evaluated against a test set before launch, and priced at a fixed fee before development starts.
Most of the cost, accuracy, and reliability decisions come down to one question: how does the model get your knowledge? There are three ways, and most production builds combine them. We decide which during discovery, not after the bill arrives.
Prompting vs. RAG vs. fine-tuning
| Dimension | Prompting | RAG | Fine-tuning |
|---|---|---|---|
| Best for | Simple, general tasks | Answers grounded in documents that change | Fixed tone, vocabulary, or one narrow high-accuracy task |
| Your data | None used | Retrieved at query time, always current | Baked into the model at training time |
| Keeps up with change | Not applicable | Update the index, no retraining | Retrain to change anything |
| Cost and latency | Lowest to start | Adds a retrieval hop, predictable | Higher upfront, can lower per-call cost at volume |
| Hallucination control | Weakest | Strong, with citations to the source | Better on domain, still needs evaluation |
Where this is heading: past a single model call, the next step is agentic systems, where the model plans, calls tools, and acts across your stack. The emerging standard for that tool access is the Model Context Protocol (MCP), which lets an agent reach your systems through one shared interface instead of a bespoke integration per tool. We build with that direction in mind, so today's product does not become tomorrow's rewrite.
Custom AI pays off when off-the-shelf output isn't good enough.
Everything on the left should already be true for your product. Even one thing on the right, and an off-the-shelf AI tool is the smarter spend right now.
A fit
01
Your product needs to generate content, process documents, handle customer queries, or automate workflows using your domain knowledge.
02
You have the data the AI needs to be accurate: documentation, records, or transactions it can be grounded in.
03
Budget for a fixed-price build, with most engagements running $50K to $150K.
Not a fit
- An off-the-shelf AI tool already covers the job well enough.
- No domain data to ground the model in, so a generic chatbot is all you actually need.
- Shopping for the cheapest GPT wrapper, not a scoped product build.
What we build
The full product, not just the API call.
Built around your data, your workflows, and your accuracy requirements. Not a generic template.
- 01Content and code generationAutomated pipelines that generate first drafts, summarize documents, extract structured data, and produce formatted output from unstructured input. Used by marketing teams to scale content, operations teams to process documents, and product teams to automate reporting and code review.
- 02Multimodal and vision AICustom pipelines for generating, editing, and processing images and documents: product image generation, document understanding from scanned files, visual data extraction, and automated media processing for content-heavy operations.
- 03Custom LLM applicationsFull-stack applications with a language model at the core: chatbots trained on your knowledge base, AI copilots for internal teams, and document assistants that understand your business context. We build the application layer, the retrieval pipeline, and the user interface, not just the LLM integration .
- 04RAG-powered document intelligenceRetrieval-augmented generation systems that ground your LLM in your actual data: product documentation, support tickets, legal contracts, medical records. The model answers from your knowledge base, with citations, rather than hallucinating from general training data. See also: RAG development .
- 05Conversational AI and chatbotsConversational interfaces trained on your knowledge base and tuned to your brand voice: customer-facing support agents, internal HR and IT assistants, and domain-specific Q&A systems. Built with guardrails, fallback handling, and human escalation paths. See also: AI chatbot development .
- 06AI agents and workflow automationAutonomous agents that execute multi-step tasks, researching, drafting, deciding, and acting across your business workflows, connected to your CRM, databases, APIs, and communication tools. See also: AI agent development .
What does your AI product actually need to do?
Tell us the business problem. We'll tell you which AI approach fits it, and what it costs to build.
How it works
How we build generative AI products
- Step 0101
Discovery and architecture design
We start by understanding the problem, not the technology. What output does your user need? What data does the AI need access to? What does accuracy look like for your use case? This shapes model selection, retrieval architecture, and validation approach.
Use case definition and accuracy requirements
Data audit: what you have and what is needed
Model and architecture selection (RAG vs. fine-tuning vs. base model)
Fixed-cost quote with milestone delivery dates
- Step 0202
Prototype in 2-4 weeks
Before full development, we build a working prototype that demonstrates the core AI capability. You can test it against real inputs, measure output quality, and give feedback before committing to the full build. This is the lowest-risk way to validate an AI approach.
Working AI prototype with core functionality
Accuracy baseline against your test cases
Feedback session and scope confirmation
- Step 0303
Data preparation and model tuning
We prepare your training data, design the retrieval pipeline, and fine-tune or configure the model. For RAG systems, this includes chunking strategy, embedding selection, and retrieval optimization. For fine-tuned models, this includes dataset curation, training runs, and evaluation.
Data cleaning, chunking, and embedding
Retrieval pipeline design and optimization
Model fine-tuning or prompt system design
Accuracy evaluation against held-out test set
- Step 0404
Product build and integration
We build the full application (the interface, the backend, the integrations) and connect it to your existing systems. The AI layer is one part of a complete product: authenticated, logged, monitored, and maintainable.
Full-stack application development
API integrations with your existing tools
Authentication, logging, and audit trail
Performance and load testing
- Step 0505
Launch and handover
We deploy to production, document the system, and hand over the codebase, infrastructure, and model configuration. You own everything and can run it without us.
Production deployment and monitoring setup
Codebase and system documentation
Infrastructure and credentials handover
Optional ongoing support retainer
Proof it works
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Amer Abu Khajil
CanadaFounder, Peak Studios & Perceptional
I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
Proof
Case studies
Fair questions, straight answers
- "You'll sell me a wrapper."
- We show you the architecture and name what's a foundation-model call versus what's actually engineered: retrieval, evaluation, guardrails. You pay the model bills directly, on your own accounts, so a hidden markup isn't even possible.
- "The output will still sound generic, not like us."
- RAG grounds every answer in your own documents and data, not the average of the public internet. That's the specific mechanism that fixes "sounds like ChatGPT wrote it."
- "It'll hallucinate in front of my customers."
- We evaluate output against a golden test set before anything ships. For customer-facing systems, guardrails make the model refuse or escalate on a shaky answer instead of confidently inventing one.
- "Someone will jailbreak it with a crafted prompt."
- For anything that touches your data or tools, we treat prompt injection as a real threat. Input is validated, the model's tool access is scoped to least privilege, and retrieved content is never trusted as instructions. The guardrail layer is part of the build, not an afterthought.
- "The token bill will double before anyone notices."
- You pay model bills directly, at cost, and we estimate the run-rate at your expected volume during scoping, so the number is on the table before you commit, not a surprise after launch.
- "I'll get locked into your build."
- You own the application code, the fine-tuned model weights, the data pipelines, and the infrastructure. No proprietary framework, nothing that requires us specifically to keep running.
- "Senior sells, junior builds."
- The engineers who scope your problem are the ones who build it. No handoff to a cheaper bench after the contract is signed.
Generative AI development cost is driven by team size, project complexity, and build length. Where you land depends on scope, not negotiation:
- Starter, $40,000-$80,000
- A single AI feature or chatbot, in 6 to 8 weeks.
- Standard, $80,000-$150,000
- A multi-feature AI product, in 10 to 12 weeks.
- Advanced, $150,000-$300,000
- Custom LLM fine-tuning and enterprise deployment, in 14 to 20 weeks.
What it costs
Fixed scope. Starting at $40,000. Yours to run.
The full product build: LLM apps, RAG pipelines, fine-tuned models, and AI agents, with the model selection that fits your constraints, scoped to your problem.
Starts at $40,000
Projects start at $40,000 and most ship in 12 weeks. Start with a single AI feature, then expand into a multi-feature product once it's proven. You own the code, model weights, and data pipelines.
An API wrapper shop connects your prompt to an LLM, wraps it in a UI, and calls it done. This is a scoped product build, starting small with one feature and expanding as it proves out, yours to run at the end.
Ownership
You own everything at project end: the application code, the fine-tuned model weights, the data pipelines, and the deployment infrastructure. No proprietary frameworks, no lock-in.
No hourly billing
For well-scoped projects, that price is locked in writing before development starts, with milestone dates and deliverables spelled out. No hourly billing, no surprise invoices.
What you actually get
The unglamorous decisions that decide whether generated output earns trust, or just looks good in a demo.
- 01
An evaluation harness, not a vibe check
Output scored against a golden test set before anything ships, so quality is a number you track, not a gut feel from the demo.
- 02
A model bill you can predict
Model choice, caching, and batching keep spend flat as usage grows, with a run-rate estimate at your volume before you commit. No mystery invoice.
- 03
Guardrails that refuse instead of invent
For customer-facing systems, the model escalates or declines on a shaky answer instead of confidently saying something wrong in front of the person paying you.
- 04
Retrieval grounded in your own data
Answers come from your documents and data, with citations where it matters, not from the average of the public internet.
- 05
A working prototype before the full build
2 to 4 weeks, tested on your real inputs, before the production budget is on the line.
- 06
Documentation your team can actually run
Architecture, prompts, and evaluation sets documented well enough that any competent engineer can run and extend it without calling us first.
Stay on topic
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Read moreFrequently Asked Questions
Generative AI development is building software around a language or image model so it produces output, text, code, images, structured data, that's grounded in your own data and brand, not the average of the public internet. It covers custom LLM applications, RAG pipelines that ground answers in your documents, fine-tuned models, and the evaluation and guardrails that keep output reliable in production. It's different from just calling an API: the model is a small part of the build, the engineering around it is the rest.
We build software that uses generative AI models to produce useful output: custom chatbots trained on your knowledge base, document automation tools that draft contracts or reports, AI copilots for internal workflows, content generation pipelines, code generation assistants, and fine-tuned models that understand your industry's language. We build the full product, not just the API connection.
Starter (single AI feature or chatbot): $40K-$80K, 6-8 weeks. Standard (multi-feature AI product): $80K-$150K, 10-12 weeks. Advanced (custom LLM fine-tuning + enterprise deployment): $150K-$300K, 14-20 weeks. Most mid-market projects land in the $50K-$150K range. We give you a fixed-fee quote before starting.
A working prototype takes 2-4 weeks. A production-ready AI product takes 8-14 weeks. Most projects: 12 weeks from kickoff to deployment. Timeline depends on data complexity, integration requirements, and whether fine-tuning is needed. We scope every project before quoting, so you know exactly what you're getting and when.
We work with OpenAI (GPT-4, GPT-4o), Anthropic (Claude 3.5), Google (Gemini 1.5 Pro), Meta (Llama 3), and Mistral. We select the right model based on your cost, latency, accuracy, and data privacy requirements. For data that cannot leave your servers, we deploy open-source models on your own infrastructure. We are model-agnostic: we recommend what fits your constraints.
Generative AI development means building a new AI product or AI-native feature from scratch: designing the architecture, training the data pipeline, and building the user-facing product. AI integration means adding AI capability (an API call, a model endpoint) to software you already have. Most buyers need one of the two. If you are unsure which fits your situation, see our generative AI integration services page.
Look for three things: named client proof with quantified outcomes (not just logos), transparent cost ranges and delivery timelines, and a working prototype before full commitment. Any company that cannot answer 'what will this cost and when will it be done' before signing is a risk. We publish our pricing tiers, quote fixed fees for scoped projects, and build a prototype in 2-4 weeks so you can validate the approach before committing to the full build.
Yes, for well-scoped projects. We give fixed-fee estimates based on a discovery call and technical scoping session. You receive a written quote with milestone dates and deliverables before we start. For exploratory builds where requirements evolve, we work on a time-and-materials basis and tell you upfront which model fits your project.
You own everything: the application code, the fine-tuned model weights, the training data pipelines, and the deployment infrastructure. We do not retain IP, use proprietary frameworks that lock you in, or create dependency on us. When the project ends, the code and models are yours to run, modify, or hand to another team.
A real generative AI development company ships products that work in production, not demos. The markers: named client references with quantifiable outcomes, production deployments rather than pilots, architecture that goes beyond chaining API calls, RAG pipelines with measured accuracy, fine-tuning experience on domain data, and engineers who have debugged latency, hallucination, and reliability at scale. An API wrapper shop connects your prompt to an LLM, wraps it in a UI, and calls it done. The difference shows up the first time something breaks in production. We have shipped production AI products since 2015, can name the clients, and can show you what they measure.
RAG is the right choice when your knowledge base changes frequently, when accuracy and citations matter, or when the model needs to answer from specific documents rather than general training data. Fine-tuning is the right choice when you need to change the model's tone or domain vocabulary, when a narrow task needs higher accuracy than prompting alone achieves, or when you need a smaller, faster, cheaper model that matches a larger model on a specific task. Most enterprise products we build use RAG for knowledge-grounded answers and optionally fine-tune a smaller model for speed and cost at production volume. We assess which fits during discovery and explain the trade-offs before you commit.
Ask to see the architecture, not just the demo. A wrapper is a system prompt and a UI in front of someone else's model. A real build has a retrieval pipeline grounding answers in your data, an evaluation harness scoring output against a test set before anything ships, and a plan for cost and latency at your real volume. Ask what's a foundation-model call versus what's actually engineered. A vendor who can only show you a polished demo, and not an evaluation result or an architecture diagram, is the tell.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Generative AI Development Company in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.