Generative AI.
Custom copilots, RAG assistants, and agents grounded in your own data, generative AI built to do real work in production, with the guardrails to be trusted.
- 6 wks
To first PoC
- 100+
GenAI builds
- 10+
Years experience
- 95%
Client retention
Generative AI that's grounded in your business
Off-the-shelf chatbots don't know your business, and raw models hallucinate. We build custom generative AI, copilots, retrieval-augmented assistants, and agents, grounded in your own data and wired into your workflows, with the evaluation and guardrails that separate a reliable system from an impressive demo.
Generic, ungrounded AI
Tools that don't know your data and confidently make things up.
Demos that don't ship
Prototypes that never survive real users, scale, or cost.
Privacy & trust gaps
Sending sensitive data to a model with no governance plan.
What generative AI covers with us
One team for the full scope. Each capability below is part of how we deliver generative AI development services.
Custom LLM apps
Applications on OpenAI, Anthropic, or open models, tuned to your domain.
RAG assistants
Retrieval-augmented chat grounded in your documents and knowledge.
Copilots
In-product assistants that help users and staff do work faster.
AI agents
Tool-using agents that automate multi-step business workflows.
Synthetic data
Generative models that augment training data where real data is scarce.
Model integration
Wire generative AI into your existing apps, data, and pipelines.
Our Generative AI process
A structured yet flexible process that turns unclear ideas into working software, with transparent collaboration and your sign-off at every stage.
- 01
Discovery
We find where generative AI creates real value and define success metrics.
- 02
Data grounding
Prepare the documents and data the model will retrieve and reason over.
- 03
Proof of concept
A working PoC in weeks to validate quality, latency, and cost.
- 04
Evaluation
Measure accuracy and safety with real evals before going live.
- 05
Guardrails
Add privacy, governance, and output controls for production trust.
- 06
Scale & iterate
Harden, deploy, and improve continuously as usage grows.
The technologies behind your build.
A proven, modern stack chosen for maintainability and scale, and what we reach for most often on generative ai engagements.
Built by people who take pride in the craft.
We refuse to ship boilerplate or spaghetti code. Here's what working with Coding Crafts means for your generative AI initiative.
Grounded, not generic
RAG and your own data mean answers rooted in your business.
Eval-driven quality
We measure accuracy and safety instead of trusting a good demo.
Privacy-first
Governance, access control, and self-hosted models where it matters.
Production focus
We build for scale, latency, and cost, the things demos ignore.
Industries we serve
Roadblocks turned into real solutions.
View all case studiesA few products we've shipped for clients who couldn't settle for excuses. Every project came with its own challenges, and here's how we solved them.
Centralizing Sales Communication with an AI-Powered CRM System
An AI-powered CRM that brings calls, WhatsApp, SMS, and email into one platform, automatically captures leads, triggers follow-ups, and gives sales managers complete visibility into pipeline performance and team activity.
View case study
Contract Review Platform
A document management system provides a variety of add-on modules, such as a contract engine and payment engine that detect discrepancies, anomalies, and potential problems within the documents.
View case study
Customer Journey Platform
The platform enables teams to create interactive walkthroughs for product flows, and guided experiences while gaining valuable insights into user interactions, engagement, and behavior throughout every digital experience.
View case study
Dental Practice Management
A complete dental business management platform that enables practices to manage procurement, equipment services, laboratory workflows, training, business analytics, and practice operations efficiently from one centralized, integrated system.
View case study
Generative AI questions, answered.
What is generative AI development?
Building applications on large language or image models, copilots, RAG assistants, and agents, grounded in your data and integrated into your workflows.
What is RAG and why does it matter?
Retrieval-augmented generation grounds a model's answers in your own documents, which dramatically reduces hallucination and keeps responses accurate and current.
How much does a generative AI project cost?
A focused proof of concept is a small fixed engagement; production systems scale with data and integration needs. We scope after a discovery call.
How long does a generative AI PoC take?
Usually two to six weeks, enough to validate quality, latency, and cost before committing to a full production build.
How do you prevent hallucinations?
We ground responses in your data with RAG, add evaluation and output validation, and set guardrails so the system stays within trusted bounds.
Can we keep our data private?
Yes. We plan governance up front and can use self-hosted or private-cloud models so sensitive data never leaves your control.
Do you fine-tune models or use prompting and RAG?
Whichever fits, often RAG and prompting get you there faster and cheaper, with fine-tuning reserved for cases that genuinely need it.
Let's build something together.
Tell us about the software you need. A real engineer reviews every brief and replies within one business day, with a clear next step, not a sales pitch.
Latest insights.
View all postsPractical essays and guides from our team on building and shipping great software.