Customer service director
- PAIN
- 70% of L1 tickets repeat; the team is burning out.
- SOLUTION
- AI chatbot (WhatsApp + web) automates the repeats, escalates the rest.
AI that ships, not a demo that pitches.
Sentinel AI (a Kali-based pentest agent), DataGreat (a zero-hallucination RAG pipeline), and EnUcuzUcak (an autonomous price-discovery system) — production AI systems Solustiq built from scratch. We speak in shipped products, not slideware.
- Hallucination target0% (DataGreat)
- Production systemsSentinel · DataGreat
- RAG pipelineCustom + Pinecone
- Model-agnosticOpenAI · Claude · Mistral
OUTCOMES
Where AI lands on the business.
- First-response automation
- 60–80%First-response automation
AI chatbot handles routine asks; escalation to humans
- Document / request throughput
- 10×Document / request throughput
OCR + classification + summarization pipeline
- Hallucinations
- 0%Hallucinations
RAG + grounded outputs + human-in-the-loop QA
- First agent in production
- <2 weeksFirst agent in production
PoC to production for a single workflow
03.A · CAPABILITIES
Making AI part of the business.
Not hype — measurable business impact. Every AI project ties to a KPI.
- 01 / 06
AI agent development
Autonomous task-executing agents for pentest, support, sales, ops. Sentinel AI architecture derivatives — real production.
- 02 / 06
LLM integration (OpenAI / Claude / Mistral)
LLM power into your existing systems: summarization, classification, translation, content generation. Model-agnostic — no vendor lock-in.
- 03 / 06
RAG implementation
Hallucination-free answers grounded in your documents, database, and content. Engineered with DataGreat's 0% hallucination discipline.
- 04 / 06
AI chatbot development (WhatsApp + Web)
A 24/7 chatbot in your brand voice, trained on your data. WhatsApp Business API, Telegram, web widget.
- 05 / 06
Custom GPT / internal assistant
Your in-house GPT for internal processes, support docs, sales playbook. Secure, controllable, audit-logged.
- 06 / 06
AI automation & business process
Offload repetitive knowledge work to an AI pipeline: document processing, email classification, invoice extraction, QA.
03.B · APPROACH
PoC to production, with a KPI.
Every AI project anchors to a business KPI and begins with measurement.
- 01
Use-case clarification (3 days)
Which business process? Which KPI? What data exists? Is AI the right tool — or is simple automation enough?
≈ 3 days - 02
PoC + data prep (1–2 weeks)
Try the model + RAG + prompt architecture on a small sample. Data cleaning, embedding.
≈ 10 days - 03
Production system (3–6 weeks)
Production-grade pipeline: monitoring, fallback, cost guardrails, human-in-the-loop, audit log.
≈ 28 days - 04
Continuous improvement
User feedback, hallucination tracking, prompt iteration, model updates. Monthly QA loop.
≈ 30 days
03.C · WHO IT'S FOR
Where AI adds value, by role.
Not hype — concrete answers to concrete pains.
Operations / HR director
- PAIN
- Document, application, email classification is manual and slow.
- SOLUTION
- Document pipeline: OCR + classification + summarization. 10× speed.
Sales / marketing lead
- PAIN
- Personalized content production doesn't scale with headcount.
- SOLUTION
- Your custom GPT — content in your brand voice, segment-based email, sales playbook.
CTO / engineering lead
- PAIN
- Engineers spend time on repetitive coding, code review, docs.
- SOLUTION
- Agent-based dev assistant: PR review, test generation, documentation.
03.D · EVIDENCE
In production. As products.
Solustiq's own production AI systems.
Sentinel AI Pentest AI built from scratch by Solustiq, integrated with the Kali Linux toolchain. Autonomous reconnaissance, vulnerability analysis, reporting; retest in the same session.
- Pentest
- AutonomousPentest
- Integrated
- KaliIntegrated
- In-house
- 100%In-house
DataGreat — Zero-hallucination RAGdatagreat.com WTTC EIR 2025-grounded travel intelligence for 42 countries. RAG pipeline, sourced answers, 0% hallucination discipline.
- Countries
- 42Countries
- Hallucinations
- 0%Hallucinations
- Launch
- AP NewsLaunch
EnUcuzUcak — Autonomous discoveryenucuzucak.com Self-training price-discovery system across 900+ airlines. Trains itself, finds the optimal route in ~15 seconds.
- Airlines
- 900+Airlines
- ML
- Self-trainML
- Search
- ~15sSearch
03.E · ENGAGEMENT
Start with PoC, scale to production.
Staged commitment for AI engagements.
AI discovery package
$ — 1 weekDoes AI fit our processes? Which use case first?
- Use-case map
- Data inventory
- Vendor comparison
- Top-3 quick-win recommendations
PoC + first agent
$$ — 4–6 weeksPoC to production for a single workflow.
- RAG pipeline design
- First agent / chatbot in production
- KPI dashboard
- First-month tuning
AI product team (retainer)
$$$$ — monthlyMultiple AI use cases, continuous improvement.
- Dedicated AI engineering team
- 2–3 new use cases shipped monthly
- Hallucination QA + monitoring
- On-call support
03.F · STACK
Model-agnostic AI stack.
No vendor lock-in — swapping models is a config change.
LLM Provider
- OpenAI
- Anthropic Claude
- Mistral
- Vertex AI
- Local (Llama)
Orchestration
- LangChain
- LlamaIndex
- Haystack
- Custom
Vector / RAG
- Pinecone
- Weaviate
- pgvector
- Qdrant
Eval / Guardrails
- Helicone
- LangSmith
- Custom QA
- Human-in-loop
Edge / Voice
- Whisper
- ElevenLabs
- WebRTC
- On-device
03.G · FAQ
Before starting an AI project.
The most frequent field questions.
How is AI integrated into a company?
Three stages: (1) pick a concrete business process (not 'let's do AI'), (2) PoC to validate data/model fit, (3) production-grade pipeline — monitoring, fallback, cost guardrails, human-in-the-loop. First production version is achievable in 4–6 weeks.
How do you measure AI ROI?
Every AI project ties to a KPI: call duration, first-response rate, conversion, error rate, manual-hours saved. The PoC sets a baseline; post-production is compared against it. Typical ROI turns positive in 6–12 months.
How do you solve hallucinations?
Four-layer discipline: (1) RAG with sourcing — the model doesn't invent, it retrieves; (2) structured output enforcement (JSON schema); (3) human-in-the-loop feedback loop; (4) eval suite for continuous measurement. DataGreat hits 0% hallucination via this discipline.
AI agent vs. chatbot — what's the difference?
A chatbot answers single questions. An AI agent executes multi-step tasks autonomously: uses tools (API calls, code execution), makes decisions, halts when needed. Sentinel AI is a typical agent — it runs the pentest process end-to-end.
OpenAI or Claude — which model should I use?
Depends on the use case. Claude (Sonnet/Opus) for code and structured tasks; GPT-4 for creative writing and fast response; Mistral or self-hosted Llama for low-cost. We build model-agnostic — swapping is a config change.
Can you build a WhatsApp AI chatbot?
Yes. Over WhatsApp Business API, in your brand voice, trained on your data — books appointments, tracks orders, answers questions, escalates to the team. Typical launch in 2–4 weeks.
Will my data leak into the model?
No. In enterprise use, OpenAI/Anthropic don't include your data in model training (contractual guarantee). In a RAG architecture your data stays in the vector DB; only relevant slices are injected into the prompt. Self-hosted Llama is also an option — fully on your servers.
What does AI cost?
Two layers: (1) build cost ($25K–$150K typical), (2) runtime cost — $0.001–0.05 per LLM call. A typical enterprise chatbot runs $500–$3K/month in LLM cost. We set cost guardrails — no surprise bills.
How long does RAG implementation take?
Data prep: 1–2 weeks. PoC: 1 week. Production pipeline + monitoring: 2–3 weeks. Typical total 5–8 weeks. Data quality is the variable — clean data shortens it.
AI agent vs. autonomous agent vs. agentic software — what's the difference?
AI agent: an LLM-driven system that executes specific tasks. Autonomous agent: an agent that decides without human intervention (Sentinel AI). Agentic software: a system where multiple agents coordinate (multi-agent). All three live in our portfolio.
RELATED VERTICALS
Where AI produces real value.
AI doesn't ship alone — it earns its keep as part of a vertical.
NEXT STEP
30 minutes before PoC.
Where will AI add value vs. where is automation enough — we sort it together. Send the brief; we'll have a discovery proposal in a week.