02 Capabilities 4 layers, one backbone

Engineered like a nervous system.

Companies don't transform through a single miracle technology, but through a layered capability stack. From data to model, model to action, action to interface — each layer its own discipline, all of them one system.

Explore the layers Talk to us
4core layers
16+sub-capabilities
80+technologies
240+proven projects
04
Interface & Channel Web · Chat · Voice · Slack · Email
03
Automation n8n · Make · Temporal · Inngest
02
Model Claude · GPT · Gemini · Llama
01
Data Postgres · Pinecone · S3 · API
01 Anatomy 02 4 Layers 03 Cross-cutting capabilities 04 Maturity 05 Tech Atlas 06 Combinations
01 · Anatomy

From data to action, one backbone.

Every layer has its own tools and its own discipline. But they all talk like a single nervous system — as data comes in, it produces decisions; as decisions are made, actions; as actions are taken, it learns.

01

Data layer

Everything the model knows comes from here. A single source of truth.

Postgres / Supabase Vector DB Object storage API connectors ETL / ELT Data quality
02

Model layer

The intelligence. Orchestrating the right model for the right task at the right price.

Claude / GPT / Gemini Embeddings Reranker Fine-tuning Eval pipeline Cost routing
03

Automation layer

From decision to action. Fault-tolerant flows triggered in milliseconds.

n8n / Make / Zapier Temporal / Inngest Retry & DLQ Human-in-the-loop Event-driven Custom serverless
04

Interface & channel layer

Wherever your users are. Web, voice, chat, alerts.

Next.js / Mobile Chat widget WhatsApp / Telegram Voice (Realtime API) Slack / Teams Email / SMS
02 · 4 Layers

Every layer is a discipline.

Each one has to be mastered on its own, but we don't sell them separately. When an AI system works, it means all four are working together.

01

Data layer

Everything the model knows comes from here.

A company that doesn't know its data can't do AI. For us, the first question isn't "which model?" but "where is the data, who owns it, what format is it in?". Collection, storage, quality, ownership — 60% of an AI project succeeds or fails right here.

Collection & ingest
  • API connectors (REST/GraphQL/SOAP)
  • CDC (Change Data Capture)
  • Event-driven streaming
  • Batch ETL pipelines
Storage
  • Postgres + Supabase (transactional)
  • BigQuery / Snowflake (analytical)
  • S3 / Cloudflare R2 (object)
  • Redis (cache + queue)
Vector & retrieval
  • Pinecone / Weaviate / Qdrant
  • Supabase pgvector
  • Hybrid search (BM25 + vector)
  • Reranker integration
Governance
  • Data lineage & audit
  • PII redaction + masking
  • Access control (RLS, ABAC)
  • Data quality metrics
Result: Every source, one source of truth
02

Model layer

The right model, the right task, the right price.

A company tied to a single model ends up with technology lock-in. The frontier model can change every month; build the orchestration layer right, and your system stays the same even when the model underneath changes.

LLM selection & orchestration
  • Claude · GPT · Gemini · Llama
  • Task-based routing
  • Fallback chain (continuity)
  • Cost / latency tradeoff
Embeddings & retrieval
  • OpenAI / Voyage / Cohere
  • Domain-specific fine-tuning
  • Multi-modal embeddings
  • Cohere Rerank integration
Fine-tuning & adaptation
  • Instruction tuning
  • LoRA / QLoRA adapters
  • DPO & RLAIF
  • Distillation (large to small)
Evals & quality
  • LLM-as-judge scoring
  • Regression eval suite
  • Hallucination control
  • Model selection via A/B testing
Result: Task-based orchestration
03

Automation layer

From decision to action. In milliseconds.

A model produces an answer, but the real value appears when that answer turns into a concrete action. Open a ticket, book an appointment, send an email, make a payment — without this layer, AI just chats.

Workflow orchestration
  • n8n (self-hosted)
  • Make.com / Zapier
  • Temporal (durable workflows)
  • Inngest (background jobs)
Event-driven architecture
  • Webhook / pub-sub
  • Kafka / NATS streaming
  • Cloudflare Queues
  • Server-Sent Events
Resilience
  • Exponential retry
  • Dead-letter queue
  • Idempotency keys
  • Saga pattern (rollback)
Human-in-the-loop
  • Approval / rejection chains
  • Threshold-based escalation
  • Audit trail (immutable)
  • Four-eyes control
Result: Action in milliseconds
04

Interface & channel layer

Wherever your users are.

Even the best system goes unused if it's delivered on the wrong channel. Users work in different interfaces in different contexts — we make sure the quality is the same on every one.

Web & mobile
  • Next.js + RSC
  • Native iOS / Android
  • PWA + push notifications
  • Embedded widgets
Conversational
  • Web chat (custom UI)
  • WhatsApp Business API
  • Voice (OpenAI Realtime)
  • Instagram / Telegram
Workplace
  • Slack bot & app
  • Microsoft Teams
  • Google Workspace
  • Email digest / report
Notifications & alerts
  • SMS (Twilio / Vonage)
  • Push (FCM / APNs)
  • PagerDuty integration
  • In-app realtime alerts
03 · Cross-cutting capabilities

Four disciplines that tie together the 4 layers.

These capabilities don't belong to a single layer — they run through all of them. Without them, the system looks like it works, but at some point it cracks.

Security & compliance

An AI system can't just be smart — it also has to be secure. Data leaks, prompt injection, jailbreaks — all real threats.

  • KVKK / GDPR / AI Act compliance
  • Encryption at rest + in transit
  • Secret management (Vault, Doppler)
  • Automatic PII redaction
  • Prompt injection defense
  • Role-based access (RBAC + ABAC)

Observability

You can't manage what you can't see. AI systems need different monitoring than classic software: tokens, cost, hallucination, drift.

  • LLM tracing (LangSmith, Helicone)
  • APM (Datadog, Sentry, Grafana)
  • Cost & latency dashboard
  • Quality regression alerts
  • Model drift detection
  • Business KPI tracking

Performance & reliability

Being AI is no excuse for being slow. The right caching, the right fallback and the right regional deployment deliver premium speed.

  • Edge deployment (3 continents)
  • Prompt caching
  • Streaming responses (SSE)
  • Fallback chain (no downtime)
  • Load testing + chaos engineering
  • SLA: 99.9% uptime, <500ms p95

Ethics & governance

"It works" isn't enough — it has to "do the right thing." Bias, explainability and human oversight are part of the system, not added on later.

  • AI usage policy
  • Bias detection & mitigation
  • Explainable AI (XAI)
  • Audit trail (immutable log)
  • Human override mechanisms
  • Ethics board reporting
04 · Maturity

AI maturity in five levels.

Every company sits at one of these five levels. Knowing which one you're at tells you where you need to go.

L1

Foundational

Not started yet

There's plenty of data, but access is fragmented. A few manual reports, operations that depend on Excel. AI isn't on the agenda yet.

Basic CRM/ERP usage
No data warehouse
No AI projects
L2

Connected

Systems are talking

Systems are connected and a data warehouse is in place. The first automations exist. There's a ChatGPT Enterprise account — some people use it.

Data warehouse (BigQuery/Snowflake)
Simple automations (Zapier)
No production AI yet
Where most companies are
L3

Optimized

Processes are automated

Internal processes are automated and the first AI projects are live. There are 1-2 successful pilots, such as a call assistant or a content generator. Not systemic yet.

n8n / Temporal flows
1-3 production AI use cases
AI governance maturing
L4

Intelligent

AI makes decisions

AI is no longer a handful of pilots — it's part of decision-making across the whole organization. Pricing, targeting, support, hiring: everywhere.

10+ production AI use cases
AI ops + governance
Multi-agent + RAG architecture
L5

Autonomous

Learning on its own

Self-improving systems: they fine-tune themselves on production data, detect performance drift and retrain, and discover new use cases.

Continuous learning pipeline
Self-healing systems
AI-driven AI development

Our service range is L2 → L4: from the moment systems start talking to the level where AI becomes part of organizational decision-making.

Get a maturity assessment
05 · Tech Atlas

Our technology atlas.

80+ technologies across 10 categories. None of them works in isolation — each is part of a stack.

AI Models
Claude Opus 4.6Claude Sonnet 4.7 GPT-5GPT-4o Gemini 2.5 ProLlama 4 Mistral LargeQwen 3Phi 4
Vector & Retrieval
PineconeWeaviate QdrantChroma pgvectorVoyage Cohere RerankBM25
Data & Storage
PostgresSupabase BigQuerySnowflake S3Cloudflare R2 RedisDrizzlePrisma
Automation
n8nMake ZapierWorkato TemporalInngest Apache AirflowPipedream
Frontend
Next.js 15React 19 Astro 5SvelteKit Tailwindshadcn/ui Framer MotionThree.js
Hosting & Edge
VercelCloudflare Workers Cloudflare PagesNetlify RailwayFly.io AWS Lambda
Communication
OpenAI RealtimeElevenLabs TwilioWhatsApp Cloud SlackTeams Deepgram
Observability
LangSmithHelicone LangfuseBraintrust DatadogSentry Grafana
Auth & Security
ClerkAuth0 Supabase AuthWorkOS VaultDoppler Cloudflare WAF
Payments & Billing
StripeIyzico PaddleLemon Squeezy RecurlyMollie
06 · Combinations

When capabilities come together.

No single capability gets the job done; specific combinations of the 4 layers create real value.

01+ 02+ 04

RAG-based knowledge assistant

Data → Embedding → Chat. A single assistant answers every question from your internal documents.

PostgresPineconeClaudeChat UI
01+ 02+ 03

Intelligent automation

A trigger comes in, AI classifies it, automation takes action. Invoice approval and ticket triage are classic examples.

WebhookClauden8nSAP
02+ 03+ 04

Voice AI assistant

Caller → realtime AI → CRM action → response. A modern assistant that fully replaces IVR.

Realtime APIElevenLabsTwilioSalesforce
01+ 02+ 03+ 04

Multi-agent system

A complex scenario that uses every layer. Multiple agents plan, act and report.

BigQueryClaude + GPTTemporalSlack + Web
01+ 02

Semantic search

Just data + model. The user searches, and RAG returns the most relevant results — search, not chat.

PostgrespgvectorVoyageCohere Rerank
03+ 04

Classic automation flow

For cases that don't need AI. Event → rule → action → notification. Sometimes the right solution is the simplest one.

Webhookn8nWhatsAppSlack
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