Custom AI That Ships
Not Another Pilot
AI built for your specific workflow, data structure, and integrations when no catalogue product fits. Frontend, backend, AI core, deployment. KPIs agreed before code; if the prototype misses them, we stop (not scale something broken).
3–5 wks
Production path
1–2 wks
Prototype in sandbox
Fixed
Scope, price, stages
What We Build
Named build types for recurring custom AI work. Open any type for scope, timeline, and pricing. Below: stabilize AI-generated code, migrate no-code workflows, and cut runaway API spend.
Stabilize, refine, & extend
Vibe Code Cleanup
Works in the demo. We fix the rest
3-5 weeks · $6,900+
Vibe Code Cleanup
Works in the demo. We fix the rest
Built fast with Cursor, Claude, or Copilot? We stabilize your codebase.
What You Get
- Codebase assessment: duplication, dead code, security gaps, flaky logic
- Prioritized cleanup roadmap (what to fix first, what to rewrite)
- Hands-on refactoring: remove cruft, consolidate duplicates, fix critical bugs
- Security audit and vulnerability patching in AI-generated patterns
- Tests and documentation so the codebase is maintainable and onboardable
No-Code Exit
Outgrew the canvas. We rebuild what hurts.
1-3 weeks · $5,750+
No-Code Exit
Outgrew the canvas. We rebuild what hurts.
Started on Zapier, Make, or n8n because it was fast. Now workflows fail quietly, fees track volume, and the diagram is the product. We migrate the critical paths to code you own. Simple glue automations can stay on the platform.
What You Get
- Workflow audit: inventory triggers, apps, volume, failure modes, and monthly platform cost
- Keep / migrate / retire matrix: what stays on no-code vs what moves to code (and why)
- Rebuild plan: data mapping, error handling, retries, and observability for each migrated workflow
- Production modules on your stack (API services, schedulers, webhooks) with tests and CI/CD hooks
- Parallel run and cutover: old and new side by side until outputs match, then decommission
- Runbook, secrets layout, and monitoring so your team can operate without the canvas
AI Cost Optimization
$5,750/month? Half wasted. We'll show you
1-2 weeks · $4,830+
AI Cost Optimization
$5,750/month? Half wasted. We'll show you
Typical result: 30-60% cost reduction without sacrificing quality.
What You Get
- API costs audit (prompt optimization, caching strategies)
- Infrastructure spending (right-sizing, spot instances)
- Model selection review (do you really need GPT-4 for everything?)
- Request volume optimization (unnecessary calls, batching)
- Data processing efficiency (storage, retrieval, preprocessing)
Shipped Custom Work
Different industries. Same formula: production system, measurable outcome, real integrations.
All casesHealthTech
0%→0%
Recognition stability, clinical grade
Blood test interpretation AI rebuilt to production grade in one month. The startup closed a seven-figure round.
Read the caseMedical Insurance
0 h→0 h
Specialist intake per week
Claims intake automated in the insurer's private cloud. 94% handled without a human, three weeks to production.
Read the caseEnterprise Tech
0 days→0 min
Quote cycle time
A 3,400-SKU catalog quoted by AI with zero engineer review. Quote capacity up 340%.
Read the caseYour bottleneck looks similar?
Scope a buildDescribe your task
We respond within one business day with an honest take on scope, timeline, and fixed-price stages.
AI Core Only
Your team owns UI and deployment
- Agents, prompts, and tool calling logic
- API integrations and data pipelines
- Tests, eval harness, and handoff docs
Complete Solution
Full stack through production handoff
- UI, API layer, AI core, and deployment
- Monitoring, alerting, and fallback logic
- Runbook and compliance documentation
Prefer email? hi@r-sun.ai
No T&M Ever
We hate it, you hate it, so let's forget it.
Fixed-Stage Payments
The prototype is a separate paid milestone. Each subsequent phase has defined deliverables, timeline, and invoice. No open-ended billing that settles at the end of a long engagement.
IP Rights
Full buyout or license for your deployment, priced differently, set in the contract before work starts. We reuse proven components and prior builds; pricing reflects scope fit, not a greenfield enterprise rebuild.
Post-Launch Support
Monitoring, incident response, and improvements are a separate retainer, not bundled indefinitely into the build price. You pay for what you actually need after launch.
How We Deliver
Most AI projects fail after handoff: no owner, metrics defined too late, pilots without fallback. We fix KPIs, scope, and stop criteria before implementation begins.
Optional
Optional
AI Consulting
Not sure whether custom development is the right path? Before scoping a build, we run a consulting engagement: audit your processes, identify where AI creates real leverage, and map the options — catalogue product, automation, or custom. You get a decision, not a roadmap to sell you more work.
- Process audit: where AI creates genuine leverage vs. where it adds complexity
- Options analysis: SaaS product, automation (n8n / Make), or custom build
- Risk and cost estimate for each path — before any code commitment
- Output: decision memo with a clear recommendation
2–5 days
Scope & KPIs
One process. One measurable target: conversion rate, SLA, cycle time, share of manual work removed. We define triggers, constraints, and human-in-the-loop checkpoints in a 1–2 page scope doc. No code until the target is written and agreed.
- Define the single metric that proves it works
- Map triggers, edge cases, and human handoff rules
- Agree stop criteria — what "fail" looks like at the prototype stage
- Output: scope doc + acceptance checklist, signed off before build starts
Paid Milestone
3–7 days
Data & Integration Audit
Every source that feeds the system: APIs, databases, file formats, permissions, data quality. We decide architecture here — RAG, direct DB validation, MCP bridge, or hybrid — based on accuracy requirements, not popularity.
- Inventory all data sources and access rights
- Assess data quality and normalisation requirements
- Choose architecture: RAG vs DB validation vs MCP vs hybrid
- Identify integration constraints before building against them
Paid Milestone
1–2 weeks
Prototype
Running in a sandbox on your real data — not a slideshow. Paid as a separate milestone so you see working output before committing the full build budget. Prototype metrics are measured against the KPIs from step 00.
- Working system on real or production-equivalent data
- First measurement against agreed KPIs
- Decision gate: proceed to production build, adjust scope, or stop
Paid Milestone
2–4 weeks
Production Build
Auth, rate limits, monitoring, structured alerts, fallback paths, human escalation for edge cases. Built for failure from day one — not because we expect it, but because production always hits what demos never show.
- Full auth, rate limiting, and security hardening
- Monitoring (Langfuse or equivalent) and structured alerting
- Fallback logic and human escalation paths for every known failure mode
- Error handling and graceful degradation throughout
Paid Milestone
3–5 days
Deploy & Handoff
Infrastructure deployment, runbook documentation, team onboarding. IP rights are defined in the contract before work starts — full buyout or non-exclusive license, your choice, not a post-delivery negotiation.
- Production deployment with CI/CD pipeline
- Runbook: incident procedures and escalation paths
- Team training on monitoring and basic operations
- Full IP transfer per agreed contract terms
You provide
- Access to data sources and APIs (read-only is enough to start)
- One decision-maker: 1–2 hours per week for check-ins
- Test cases and acceptance scenarios for each milestone
We deliver
- Repository with full documentation and test coverage
- Deployment with monitoring, alerting, and fallback logic
- Integration runbook and incident escalation procedures
Stop Rule
Iterate or Stop
Gate · Per KPIs
If the prototype misses KPIs — we stop. No sunk-cost rollout to "give it more time." We reassess: different scope, different architecture, or a different solution entirely. If KPIs are hit — next scope is negotiated as a fixed stage.
What We Decline
We turn down projects we cannot do well. Here is what that looks like.
"Give us ChatGPT for the whole company"
Without a defined process and measurable outcome, this fails, expensively.
Free pre-sales architecture
A scoping call and short proposal are free. But system design, data flow diagrams, and effort estimates are paid work (that's Phase 0). We don't spend weeks on architecture under promise of a contract later.
Custom ML model training
Designing and training proprietary models from scratch is data science, not AI integration. We build on top of foundation models. For custom training pipelines we bring in specialist partners. Ask us to connect you.
Equity or deferred payment deals
Build now, get paid if it works: we don't take risk on your business model. We work against agreed milestones at market rates. We build only what we control end-to-end: scope, stack, and delivery.
Before committing to a build, consider:
FAQ
What's the difference between custom development and AI consulting?
Consulting is strategy and architecture. It may conclude “don't build, buy X instead” or “fix your process first.” Custom development is execution: we write code, deploy, and ship. We keep them separate so consulting stays objective. We won't recommend building just to sell you development. If you're unsure which you need, start with a consulting engagement first.
Why not just use a ready-to-run agent from the catalogue?
If a catalogue product covers your use case, we'll say so before scoping custom work. Booking Agent, Talkulate AI CPQ, and Career Finder are faster to deploy and cost less when the workflow matches. Custom makes sense when your data structure, integration requirements, or process logic is specific enough that adapting a product is harder than building right from the start.
Do I have to pay before knowing if it works?
The prototype is a separate paid milestone, typically 1–2 weeks, explicitly priced before it starts. You pay for the prototype, see working software on your real data, and only then decide whether to proceed to the full build. This is the only point where the decision is yours before committing to the larger investment. Rare exception: a tight scope with a previously proven stack where prototype and production are practically the same deliverable.
Who owns the code after delivery?
Rights to the delivered solution are set in the contract before work starts: full buyout (you own everything) or a license for your use (we may reuse architecture patterns elsewhere), with different pricing for each. Pricing is not a greenfield six-figure build; we reuse proven components and prior delivery work, so you pay for fit to your workflow and data, not for inventing the stack from scratch.
Can you work alongside our in-house engineering team?
Yes. We can own the AI layer while your team owns infrastructure, or pair on architecture decisions and code review. Works best with a clear ownership boundary: we own X, you own Y, weekly sync. We don't disappear into a silo. Progress is visible at every check-in.
Can you integrate with our existing ERP, CRM, or other business systems?
Yes, and we assess exactly this in Phase 01 (Data & Integration Audit) before any code is written. Common systems we work with: SAP, Salesforce, HubSpot, Microsoft Dynamics, NetSuite, custom databases, and internal APIs. Read-only access is enough to start. We map data sources, access rights, and data quality in the audit phase so there are no integration surprises during the build.
What happens if KPIs aren't met at the prototype stage?
We stop. No sunk-cost rollout to give it more time. We reassess: different scope, different architecture, or sometimes a different solution entirely. You've spent prototype budget (days, not months) and you have real data about what the system can and cannot do. That is more valuable than discovering the same thing six weeks into production.
Ready to Scope a Build?
No 30-page proposals. We look at your bottleneck and tell you honestly whether custom development is the right call, or whether something else gets you there faster.
Prefer email? hi@r-sun.ai
Tell us: 1) the bottleneck, 2) what you've tried, 3) rough timeline and budget.