We were running everything across spreadsheets and WhatsApp groups. Felukaa built us one system in three weeks — now every lead, every reservation, every agent is in one place. Nothing slips through anymore.
[ AI INTEGRATION ]
AI that plugs in.
Not a chatbot bolted on the side.
AI models that can read your own documents and data, wired into systems you already run. Document Q&A, contract review, support triage, lead scoring, internal knowledge search — automated where it pays off, a human checking the important calls where it matters.
[ THE PROBLEM ]
Most “AI integrations” are demos in disguise.
Vendors and consultants pitch AI like it’s a turn-key add-on. In reality, off-the-shelf AI bolted onto your tools pulls up the wrong customer’s contract, leaks data through the wrong connection, and stops working the moment the underlying model updates.
Real AI integration is plumbing — answering from YOUR data, checking against YOUR rules, asking a human when it is unsure, and watching the costs. Most projects skip the plumbing and ship the demo. Then it breaks in week three.
[ WHAT YOU GET ]
Concrete deliverables. No surprises.
With Felukaa
- We pick a use-case with a clear payoff before building
- AI that answers from your own documents and data
- Built on trusted models (OpenAI, Claude, or open-source)
- Guardrails so it does not make things up
- A human check for anything important
- Monitoring and cost tracking after launch
Off-the-shelf
- Generic chatbot that makes things up about your data
- Provider lock-in — can’t switch OpenAI → Claude → open-source
- Token costs you can’t predict, monitor, or cap
- No audit trail when AI gives wrong answers
- Customer data sent to outside services you don’t control
- “AI” that’s just a sloppy ChatGPT wrapper
[ THE MECHANISM ]
Use-case scoping before model selection.
01
Use-case + ROI baseline
We define what success looks like in real numbers — minutes saved per task, tickets deflected, response time cut. If the math doesn’t justify the build, we say so before we start.
02
Retrieval pipeline
Your documents and data made searchable so the AI answers from YOUR truth, not from whatever it picked up in training. Made-up answers drop sharply.
03
Guardrails + validation
Answers checked against your rules, defenses against people trying to trick it, and filtering of sensitive data. A human reviews anything it is unsure about. Plus monitoring and cost dashboards.
04
Production handoff
Deploy alongside your existing systems — CRM, support tools, internal portals. Model + provider can be swapped (OpenAI → Claude → open-source) without rewriting the integration.
[ PROOF ]
Elite Gouna CRM — AI in production.
Real-time lead scoring on every inbound enquiry (engagement signals, behavioral patterns, deal-likelihood). WhatsApp reply drafts auto-generated for agents — they edit and send, instead of typing from scratch. Both running in the Elite Gouna CRM for over a year. Faster response times, no made-up property details, full audit trail.
1,754+
Leads scored
~70%
Reply drafts kept
12 mo+
In production
0
Made-up facts shipped
[ FROM THE TEAM USING IT ]
How their team actually uses it.
[ START ]
Book a free 15-min consultation.
Tell us where AI would actually pay off in your operation. We’ll send back a scoped plan — what’s worth building, what isn’t, and a realistic ROI baseline.
[ FAQ ]
Frequently asked.
Which AI providers do you work with?
OpenAI (GPT-4 / o-series), Anthropic Claude, Google Gemini, and open-source models you can run on your own servers when data has to stay in-house. We pick the provider per use-case — not “whichever has the best brand recognition.” Many clients run multi-provider with fallback for cost + reliability.
How do you stop it making things up?
Three layers: the AI answers from your own data (not from training), every answer is checked against your business rules, and anything it is unsure about goes to a person. We measure how often it gets things wrong before and after launch — it’s a tracked number, not a hope.
What does this cost to run monthly?
Depends on volume and provider. We build with cost monitoring + caching from Day 1, so you can see exactly which use-cases pay for themselves. Typical client spends $50-$500/month on inference; high-volume support deflection cases can be $2k+ but generally save multiples of that in agent time.
Can we keep our data on your own servers?
Yes — open-source models you can run on your own servers or private cloud. We’ve built document-reading AI that never touches an outside provider. A bit slower than the big closed models, but completely under your control.
What about people trying to trick the AI?
Active concern, not a footnote. We layer input sanitization, structured output formats, content filtering, and rate limiting. Production AI surfaces are security-tested before launch. Public-facing chat is different from internal document-reading AI — we scope security to the actual threat model, not a generic checklist.