Why CodeHazel
You've Outgrown Your No-Code AI Builder
You've Outgrown Your No-Code AI Builder
Tools like Base44 or Lovable are genuinely good for testing an idea fast. They start showing their limits once you need real data ownership, custom integrations, or something that scales past a few hundred users. We pick up right where those tools stop.
- Migration from AI app builders to owned, custom codebases
- Full data portability, no vendor lock-in
- Architecture built for the scale you’re actually planning for
Your Chatbot Frustrates People Instead of Helping Them
A chatbot that can't answer anything outside a rigid script is worse than no chatbot at all — it just adds a step before someone gives up and emails support anyway. We build ones trained on your actual knowledge base, with a clear handoff to a human when it genuinely doesn't know.
- Trained on your real documentation and support history
- Clear escalation path to a human, not an infinite loop
- Measured on resolution rate, not just conversation count
Your Team Is Still Doing By Hand What Software Could Do
Manual data entry, copy-pasting between tools, chasing approvals over email — most of it is automatable, and most teams have just gotten used to it. We map the actual workflow first, then automate the part that's genuinely repetitive.
- Workflow audit before any automation gets built
- Integrations across the tools you already use
- Built to flag exceptions to a human, not fail silently
What We Do
AI, Automation & SaaS Services for Wherever You Are in the Process
Some of you already know exactly what you need built. Others are still comparing a no-code AI tool against hiring a developer. Either way, here's the full range of what we build — each links to a dedicated page once you're ready to go deeper.
AI SaaS Development
AI SaaS Platform Development
A SaaS product with AI features baked into the core workflow, not a chatbot widget bolted onto the corner of the screen. We build the AI to actually change how the product works, since that's usually the difference between a feature and a gimmick.
AI Chatbots
Custom AI Chatbot Development
Chatbots trained on your actual product and support content, not a generic script that falls apart the moment someone asks something slightly off-topic.
No-Code Migration
Base44, Lovable & No-Code App Migration
Built something with Base44 or Lovable and hit the ceiling on custom logic, data ownership, or scale? We take what you've validated and rebuild it properly, without starting the product thinking from zero.
Workflow Automation
Business Process & Workflow Automation
The repetitive stuff — data entry, approvals, reporting, syncing between tools — mapped out and automated so your team spends time on the parts of the job that actually need a person.
LLM & AI Agents
LLM Integration & Custom AI Agents
AI agents that can actually take action — pull data, trigger a workflow, update a record — not just generate a paragraph of text and stop there.
RAG & Knowledge AI
RAG & Custom Knowledge Base AI
An AI that answers from your actual documents and data, not from whatever the model was trained on two years ago. This is the difference between an AI that's occasionally right and one your team can actually trust.
SaaS MVP Development
AI-Enhanced SaaS MVP Development
A lean, fundable SaaS MVP that leads with the AI feature that's actually your differentiator, instead of a generic app with an AI tag added to justify the valuation.
Predictive Analytics
Predictive Analytics & Machine Learning
Models built on your actual historical data to forecast demand, flag churn risk, or catch anomalies — trained and validated against real outcomes, not just a demo dataset that looks good in a pitch.
Document Automation
Document Processing & Computer Vision Automation
Invoices, forms, and scanned documents extracted and processed automatically, cutting out the manual re-typing that eats an afternoon every week.
Internal Tools
AI-Powered Internal Tools & Dashboards
Internal dashboards that surface what actually matters instead of a wall of numbers nobody checks. Built around the decisions your team needs to make, not a generic reporting template.
CRM & Integration
CRM & Third-Party Integration Automation
Your CRM, support tool, and billing system talking to each other automatically, so nobody's manually copying a lead from one system into another anymore.
AI Consulting
AI & Automation Consulting
For teams that need an honest technical read on whether AI actually solves their problem before committing a development budget — including when the answer is "not yet."
Have an Idea, a No-Code Prototype, or a Manual Process Eating Your Week? Start Here..
Not everyone's starting from a blank slate. Tell us where you actually are, and we'll pick up from that point instead of restarting the conversation from zero.
A Process Built Around Whether the AI Actually Helps
We look at what you're actually trying to solve and tell you honestly whether AI or automation is the right tool for it. Sometimes the answer is a simpler rules-based system, and we'd rather say that upfront than build something over-engineered.
Before full development, we build a working prototype of the core AI or automation logic, so you can see it operating on real (or realistic) data before committing to the full build.
Development runs in sprints, with the AI or automation component tested against real edge cases as it's built, not validated once at the very end when a fix costs a lot more.
We deploy, then keep watching how the AI or automation performs against real usage, since model drift and edge cases nobody anticipated tend to show up in the first few months, not on day one.
Built to Keep Working After the Demo EndsAfter the Demo Ends
From Idea to Working AI, Without the Hype Cycle
Feasibility, data readiness, and architecture get worked out before development starts, so you're not discovering halfway through that the data you have isn't actually enough to train what was promised.
Tested Against Real Edge Cases, Not Just the Demo
Every model and automation gets tested against the messy, inconsistent data your business actually produces, not the clean dataset that made the pitch look good.
Built to Handle More Data and More Users Later
An AI feature that works fine on your first 1,000 records can behave very differently at 100,000. We architect for the scale you're planning toward, not just the pilot.
Watching for Drift After Launch, Not Just Before
Models degrade as real-world data shifts away from what they were trained on. Our support plans catch that early, instead of you noticing months later that accuracy has quietly dropped.
AI and Automation Tools Chosen for What Actually Works
The right model or framework depends on your data and your budget — we pick the stack based on what the problem actually needs, not whichever model is trending.
We don't default to the flashiest model. We use the one that fits your data, your budget, and your actual use case.
The underlying models powering the AI features we build.
Frequently Asked Questions
The questions we hear most — including the honest one about whether a no-code AI tool might actually be enough for you.
How much does custom AI or automation development cost?
A single automation workflow or a basic custom chatbot typically runs $500 to $20,000. A more complete AI SaaS platform or a custom AI agent with multiple integrations usually lands between $30,000 and $100,000+, depending on how much custom model work and integration is involved. We'll give you a real number after understanding your actual data and use case.
Cost breakdown:
- Single workflow automation or basic chatbot: $500 – $20,000
- Custom AI SaaS feature or AI agent: $30,000 – $100,000+
- Full AI SaaS platform build: $100,000+
Should I use a no-code AI builder like Base44 or Lovable instead of hiring a developer?
For testing an idea fast or building something simple, honestly, yes — those tools are good at what they do, and we'd rather you validate cheaply than pay us to build something the market doesn't want. The point where custom development starts making more sense is usually when you need real data ownership, custom logic those platforms don't support, or a product that has to scale past a few hundred users.
Quick way to decide:
- Testing an idea, limited budget: no-code builder is probably fine
- Need custom logic, data ownership, or real scale: custom development
- Already hit a wall with a no-code tool: that's usually the signal
How long does it take to build a custom AI feature or automation?
A single automation workflow usually takes 3 to 6 weeks. A custom AI chatbot or agent typically takes 6 to 10 weeks depending on how much of your own data it needs to be trained on. A full AI SaaS platform is a longer build, usually 3 to 6 months.
Timeline breakdown:
- Weeks 1-2: Discovery, data readiness & architecture
- Weeks 3-6: Development & model/workflow testing
- Weeks 7-8: Integration, edge case testing & refinement
- Weeks 9-10: Launch & monitoring setup
Can you migrate my Base44 or Lovable app to a custom codebase?
Yes, and it's a more common request than people expect once a no-code build starts hitting real limits. We migrate your data and rebuild the logic those platforms can't support, without asking you to start the product thinking over from scratch.
What migration typically includes:
- Full data export and migration, nothing left behind
- Rebuilding custom logic the no-code platform couldn't support
- Architecture built to scale past where the original tool capped out
Do you build with our existing data, or does the AI start from scratch?
With your existing data, wherever possible — a model or chatbot trained on your actual documents, support history, or business data is almost always more useful than a generic one. Part of our discovery process is assessing whether your data is actually ready for that, and cleaning it up first if it's not.
What we check first:
- How much usable data you actually have
- Whether it needs cleaning or structuring before training
- What's missing that would need to be collected first
What happens if the AI gives a wrong or unexpected answer after launch?
It happens, especially early on, which is why we build in monitoring and a clear escalation path to a human rather than assuming the AI will always be right. We also watch for model drift over time, since an AI that was accurate at launch can quietly get less accurate as real-world data shifts.
Post-launch safeguards include:
- Human escalation path for anything the AI can't handle confidently
- Ongoing accuracy monitoring, not a one-time test
- Regular retraining or adjustment as your data changes
Didn't Find Your Answer?
AI and automation projects vary a lot in scope, and a FAQ can only go so deep. Tell our team what you're actually trying to solve — including if you're not sure AI is even the right tool for it — and we'll give you a straight answer.