4.9 Based on 12 audits
Your product needs AI.
Your team can't build it.
RAG, agents, copilots for your SaaS — shipped in 4-6 weeks by senior AI engineers. Fixed price. Async. Production-grade.
Built with production-grade AI infrastructure
OpenAI
Claude
LangChain
Vercel AI
Pinecone.01
80% of AI projects fail. Here's why:
No AI engineer on staff
$200K+/yr to hire, 3-6 months to fill the role. Your product can't wait that long.
Internal team tried, got stuck
Hallucinations, cost overruns, latency issues. Building AI features is a different discipline.
Agency quoted $100K+
And a 6-month timeline with weekly status calls. You need results, not meetings.
Freelancer delivered a demo
That broke in prod. No monitoring, no guardrails, no eval suite. Just a notebook.
.02
One studio. 4 weeks. Production-grade.
We build AI features that work in the real world, not just in notebooks.
RAG pipelines
Retrieval-augmented generation with your data. Accurate answers grounded in your knowledge base.
AI agents & agentic workflows
Autonomous task execution with tool use, planning, and human-in-the-loop controls.
LLM integration into your product
Structured outputs, function calling, streaming responses wired into your existing stack.
AI copilots (in-app)
Context-aware assistants embedded in your product UI that help users get things done faster.
AI-powered search
Semantic search, hybrid retrieval, and reranking that actually understands user intent.
Intelligent automation
Classification, extraction, summarization, and routing that replaces manual workflows.
.03
How it works
01
AI Audit
$2,999
We map your AI opportunities, assess data readiness, and deliver a prioritized roadmap.
02
AI Sprint
$14,999+
We build and ship one production-grade AI feature in 4-6 weeks.
03
AI Crew
$4,999/mo
We iterate, optimize, and ship new AI features continuously.
.04
Transparent pricing.
No proposals. No scope creep. Pick a tier, pay via Stripe, we start building.
AI Audit
Instant start“We want AI but don’t know where to start.”
Explore feasibility before committing
$2,999
1-2 weeks
- Full stack assessment
- AI opportunity map
- Data readiness audit
- Prioritized roadmap
- Cost projection
- Architecture recommendations
AI Sprint
Discovery call“We tried internally and got stuck.”
One AI feature, production-ready
$14,999
4-6 weeks
- 1 AI feature in production
- Architecture docs
- Prompt library (versioned)
- Integration tests + eval suite
- Monitoring setup
- Deployment runbook
- Handoff docs + Loom walkthroughs
AI Sprint+
Discovery call“We need multi-agent or complex RAG.”
Advanced AI architecture
$19,999-$29,999
4-6 weeks
- Complex multi-agent systems
- Multi-source RAG pipelines
- Everything in AI Sprint
- Advanced guardrails config
- Custom eval framework
- Performance optimization
AI Crew
Discovery call“Keep shipping AI features every month.”
Ongoing AI engineering capacity
- Ongoing AI engineering
- New features + iterations
- Model upgrades & optimization
- Cost monitoring & reduction
- Priority async support
- Pause anytime
We take max 2 AI sprints per month. All slots are first-come, first-served.
.05
Every sprint ships with:
- Working AI integration deployed to staging/prod
- Architecture docs (system diagram, data flow, model choices)
- Prompt library (versioned, tested, documented)
- Integration tests + evaluation suite
- Deployment runbook
- Monitoring setup (LangSmith/Langfuse)
- Guardrails + safety config
- Handoff docs + Loom walkthroughs
- Cost projection + optimization recommendations
.06
How we compare
.07
What founders say
“We tried building RAG internally for 3 months. Kactuz shipped a production-grade pipeline in 4 weeks that actually handles edge cases.”
CTO
Series A, Legal Tech
“Our AI feature was hallucinating on 30% of queries. They rebuilt the retrieval layer, added guardrails, and got it under 2%.”
VP Engineering
$8M ARR, HR Tech
“The AI Audit showed us we were spending 4x more on OpenAI than needed. The optimization alone paid for the engagement.”
Technical Founder
Seed, E-commerce SaaS
Client details anonymized. Real results from completed engagements.
.08
Before & After
Before
SaaS founder wanted AI features but didn't know where to start. Considered hiring a $220K AI engineer. No data pipeline, no embeddings, no eval framework.
After (1 week)
Complete AI opportunity map: 4 high-ROI features identified, data readiness assessed, architecture recommended, cost projection ($800/mo vs $4K/mo). Saved $200K+ vs premature hire.
E-commerce SaaS — AI Readiness
Deliverable: Audit report + roadmap + cost model
Before
Basic OpenAI wrapper with no retrieval. Hallucinating on 30%+ of company-specific questions. $4K/mo in API costs for 500 users.
After (4 weeks)
Production RAG with pgvector, hybrid search, reranking, citation grounding. Hallucination rate under 2%. API costs down 75% with caching + model routing.
AI-Powered Knowledge Base
Stack: Next.js, LangChain, pgvector, OpenAI, Vercel AI SDK
Before
Support team manually processing 200+ tickets/day. Categorization took 3 hours. Routing errors caused 40% of escalations.
After (5 weeks)
AI agent with tool-calling: auto-categorizes, routes, and drafts responses. Human-in-the-loop for edge cases. Processing time: 3 hours → 15 minutes.
Intelligent Support Automation
Stack: TypeScript, Claude API, LangGraph, Trigger.dev
Before
Shipped AI search feature via Sprint. But models update monthly, prompts drift, and users find new edge cases. No one on the team to maintain it.
After (4 months ongoing)
Monthly model upgrades (GPT-4o → Claude 4), prompt optimization from production data, 2 new AI features shipped, API costs reduced 40% via smart caching.
Legal Tech SaaS — Continuous AI
Stack: Next.js, LangChain, Anthropic API, pgvector, Redis
.09
FAQ
Hey, I'm Gustavo. 👋
I founded Kactuz after 7+ years building fintech platforms, marketplaces, and SaaS infra. 15+ products shipped.
In 2025, I watched the vibe coding wave explode. Founders shipping MVPs in hours with Cursor and Bolt. Beautiful frontends. Broken backends. Leaking data. Zero auth.
So we built the cleanup crew. Same AI tools, senior judgment on top. No calls, no BS — just code that works in production.
If your backend is on fire, we can help. If it's not on fire yet, get an audit before it is.
Gustavo Henrique
Founder, Kactuz · Belo Horizonte, Brazil 🇧🇷
Need to fix your AI-generated backend first?
If your codebase needs stabilization before adding AI features, our Vibe Code Rescue track can help.