AI Integration & LLM Apps

Ship AI that actually works in production . First demo in 10–14 days.

Q: How long does an AI integration take?

A working demo/pilot takes 2–4 weeks. Full production build typically 4–10 weeks depending on complexity, data volume and number of integrations. We always start with a Discovery Sprint to lock the scope.

Q: How much does an AI integration cost?

It depends on scope. A Discovery Sprint starts from €3–5k. Pilot/PoC from €10–20k. Full production build from €25–60k+. We provide a detailed, free estimate after a Discovery call — no obligations.

Q: Is my data safe?

Yes. NDA and DPA signed before data access. Data stays on your infrastructure. We apply RBAC, audit trails, and data minimization by default. GDPR compliance and data privacy are part of the architecture, not an afterthought.

Q: How do you control hallucinations?

Through a multi-layer eval pipeline: automated accuracy tests, LLM-as-judge scoring, human-in-the-loop reviews and production hallucination monitoring with alerting. Our target is < 2–3% hallucination rate.

Q: Can I use my own on-premise models?

Yes. We support on-premise deployments with Llama 3, Mistral and other open-weight models. Cloud, hybrid or fully on-prem — architecture is model-agnostic by design.

Q: What if the AI gives wrong answers?

We build guardrails: confidence scoring, fallback to human review, automatic flagging of low-quality responses. The eval pipeline catches regressions before they reach users.

Q: Do you integrate with our CRM/ERP?

Yes. We've integrated with Salesforce, HubSpot, SAP, custom ERPs and legacy APIs. The data connectors are built as modular components that can be extended or replaced.

Q: What does maintenance look like?

Ongoing monitoring, model updates when new versions are released, drift detection, cost optimization reviews and priority support. We offer SLA-based maintenance packages.

RAG, agents, tool-use — production-grade, not a demo
Token cost control — routing, caching, monitoring (40–70% savings)
Your data stays on your infrastructure (NDA + DPA + GDPR)
You own the code. Zero vendor lock-in.

Eval pass rate

92%

Hallucination rate

<2%

Token cost / req

€0.21

Book a discovery call See packages

No obligations. NDA on request.

Demo 10–14 days
Token cost transparent
Zero lock-in

Trust

Evaluated, not hyped.

Eval-first delivery

Every release proven against eval suite
30-day sprint to production

Discovery → demo → live
Private by default

NDA + DPA + your VPC
SLA-backed support

On-call coverage post-launch

See the process (3 min) →

Cost of inaction

Everyone's shipping AI. Most of it doesn't work in production.

Token costs grow 10× without smart routing and caching
Manual eval eats 40+ engineering hours per month
One hallucination in production = reputation and legal risk
Without a monitoring pipeline, problems emerge after users complain
Your team experiments in notebooks. Your competitor ships to users. The gap widens every sprint.

What does a month without AI architecture cost?

Token costs without routing	€2–5k/mo
Time on manual eval	40+ hrs/mo
Hallucination / data leak risk	priceless
Roadmap blocked by AI debt	€5–15k/mo

€4,000 – €25,000 / month wasted

What we do

NEURAL: the six layers of production AI.

RAG
RAG & Data Integration

We connect LLMs with your databases, documents and APIs. Retrieval-Augmented Generation with vector search, chunking and re-ranking.

Recall ≥ 0.85 baseline
AGENTS
Agentic Automation (MCP)

Autonomous AI agents that call tools, browse APIs and execute multi-step workflows. Built on the Model Context Protocol for interoperability.

Eval-driven loop, no chaos
LLM APPS
LLM Apps (Web/Mobile)

Full-stack AI applications with chat, search, summarization or content generation. Production-grade UX with streaming responses.

Streaming + retry built-in
EVAL
Quality Evaluation (Eval)

Automated eval pipelines that measure accuracy, hallucination rate and relevance. LLM-as-judge, human-in-the-loop and regression tests.

Regression catch ≥ 95%
COST CONTROL
Cost Control (Routing/Cache)

Smart model routing, prompt caching and token budgeting. We reduce API costs by 40–70% without sacrificing quality.

Token spend dashboards
MONITORING
Monitoring & Security (RBAC)

Tracing, logging, cost dashboards, RBAC and audit trails. Full observability of every LLM call in production.

p95 latency + drift alerts

Hard proof

Before / after. Real shipments.

Eval pass rate

BEFORE

61%

AFTER

92%

+31 pp after 30-day sprint
Latency p95

BEFORE

6.4s

AFTER

1.8s

−72% — streaming + caching
Cost per request

BEFORE

€1.4

AFTER

€0.21

−85% — model routing + cache

neural.eval.log


    rag_accuracy
    =
    94.2%
  

    hallucination_rate
    =
    < 2.1%
  

    avg_response_time
    =
    230ms
  

    cost_per_query
    =
    $0.003
  

    eval_score
    =
    91/100

Process

Engineering process. Zero 'we'll see'.

Six steps from data audit to production AI. Each with a clear deliverable.

01 Week 1
Discovery & Data Audit

We audit your data sources, define use cases and map the AI opportunity landscape.
02 Week 2
Architecture & PoC Design

System architecture, model selection, RAG design, eval strategy. Blueprint before code.
03 Weeks 2–3
Pilot / Demo

Working prototype with your real data. Stakeholder demo, eval results, go/no-go decision.
04 Weeks 3–6
Production Build

Full system with RBAC, monitoring, cost controls, CI/CD. Hardened for production traffic.
05 Week 6
Hardening & Eval

Eval suite green-lit, load tested, security scanned. SLA targets confirmed before traffic.
06 Ongoing
Maintenance & Monitoring

Ongoing: model updates, drift detection, cost optimization, SLA monitoring.

Definition of Done

NDA signed before data access
DPA / GDPR compliance verified
RBAC & audit trail in production
Automated eval pipeline running
Hallucination monitoring active
Cost alerting configured

Packages

Pick your level of ambition.

Spike

7 days

Data audit + RAG hypothesis + estimate
- Data source audit & quality assessment
- Use case mapping & prioritization
- RAG architecture hypothesis
- Model selection recommendation
- Detailed cost estimate
Start Spike
RECOMMENDED

Sprint

30 days

Pilot to production-grade rollout
- Everything in Spike
- Working RAG/agent prototype + stakeholder demo
- Eval pipeline with baseline metrics + go/no-go recommendation
- Production-grade RAG/agent system
- RBAC, audit trail, security hardening
- Cost controls (routing, caching, budgets)
- CI/CD pipeline + monitoring
- Full code handoff & documentation
Run the Sprint
Guardian

Monthly retainer

Eval-driven evolution + on-call SLA
- 24/7 monitoring & alerting
- Model updates & drift detection
- Cost optimization reviews
- Eval regression monitoring
- Priority support SLA
Enable Guardian

Final price depends on scope. Free estimate after Discovery call.

Scope

What strongly affects the price

Data volume and complexity (documents, databases, APIs)
Model mode: cloud API vs on-premise deployment
SLA level and uptime requirements
Number and complexity of integrations (CRM, ERP, legacy systems)

What we DON'T do

AGI or science-fiction promises
Chatbots without a clear business goal
"AI for the sake of AI" projects

Common concerns

The questions every CTO asks first.

Our data can't leave the building.
Understood. Models run inside your VPC (AWS / Azure / GCP) or on-prem. Repository on your GitHub/GitLab. We sign NDA + DPA + GDPR before any data access — standard from day one, not an option. We minimize access to the bare minimum and audit-trail every read.
What about hallucinations?
Eval-driven from week one. Automated eval suite measures hallucination rate, retrieval grounding and structured-output validity on every release. Baseline target: <2%. Anything above triggers regression alarms before the deploy hits prod.
What if the model gets deprecated?
Model-routing layer abstracts vendors. OpenAI, Anthropic, Llama, Mistral — swap any provider without code changes. Zero vendor lock-in is by design, not a marketing line. The eval suite catches regression after the swap.
What if the quality regresses after launch?
Guardian retainer covers eval-driven regression detection on every model push. RBAC + audit trail on every production deployment. Cost + drift alerts wake on-call before users notice. SLA-backed — not best-effort.
Can't we just use ChatGPT + a plugin?
For internal play — sure. For production: enterprise SOC2/GDPR boundaries, observability, eval-driven regression, multi-tenant cost control and 40–70% token savings via routing don't ship in consumer plugins. NEURAL is the difference between a tech demo and an SLA.
Who owns the code at the end?
You. Repository on your GitHub/GitLab from day one. Full code ownership — your repo, your IP. Full documentation handed off: architecture, runbook, API reference. Zero vendor lock-in: swap models or providers at any time.

Free tools

Pressure-test your AI idea before you call us.

Build vs. buy? How much will your RAG pipeline cost? Use our free AI calculators to make data-driven decisions.

Tools & stack

The toolbox behind every NEURAL sprint.

OpenAI GPT-4o
Claude
Gemini
Llama 3
Mistral
Pinecone
pgvector
Qdrant
ChromaDB
Embeddings API
LangChain
LlamaIndex
Semantic Kernel
CrewAI
MCP

Next.js
Node.js
Python
FastAPI
React
LangSmith
Helicone
Tracing
Prometheus
Docker
Kubernetes
AWS Bedrock
Azure OpenAI
GCP Vertex

From day one you get: your repository, full documentation, infrastructure-as-code and the freedom to swap models or providers. Zero vendor lock-in.

FAQ

Quick answers from the engineering side.

How long does an AI integration take?

A working demo/pilot takes 2–4 weeks. Full production build typically 4–10 weeks depending on complexity, data volume and number of integrations. We always start with a Discovery Sprint to lock the scope.

How much does an AI integration cost?

It depends on scope. A Discovery Sprint starts from €3–5k. Pilot/PoC from €10–20k. Full production build from €25–60k+. We provide a detailed, free estimate after a Discovery call — no obligations.

Is my data safe?

Yes. NDA and DPA signed before data access. Data stays on your infrastructure. We apply RBAC, audit trails, and data minimization by default. GDPR compliance and data privacy are part of the architecture, not an afterthought.

How do you control hallucinations?

Through a multi-layer eval pipeline: automated accuracy tests, LLM-as-judge scoring, human-in-the-loop reviews and production hallucination monitoring with alerting. Our target is < 2–3% hallucination rate.

Can I use my own on-premise models?

Yes. We support on-premise deployments with Llama 3, Mistral and other open-weight models. Cloud, hybrid or fully on-prem — architecture is model-agnostic by design.

What if the AI gives wrong answers?

We build guardrails: confidence scoring, fallback to human review, automatic flagging of low-quality responses. The eval pipeline catches regressions before they reach users.

Do you integrate with our CRM/ERP?

Yes. We've integrated with Salesforce, HubSpot, SAP, custom ERPs and legacy APIs. The data connectors are built as modular components that can be extended or replaced.

What does maintenance look like?

Ongoing monitoring, model updates when new versions are released, drift detection, cost optimization reviews and priority support. We offer SLA-based maintenance packages.

AI/LLM Glossary

RAG (Retrieval-Augmented Generation): An architecture pattern where an LLM generates answers grounded in retrieved enterprise data, reducing hallucinations and ensuring up-to-date responses.
LLM (Large Language Model): A deep learning model trained on massive text corpora that can understand and generate human-like text. Examples: GPT-4, Claude, Llama 3.
Embedding: A numerical vector representation of text that captures semantic meaning, enabling similarity search and retrieval in RAG systems.
Eval (Evaluation): Systematic measurement of LLM output quality using automated metrics (accuracy, relevance, hallucination rate) and human review.
Hallucination: When an LLM generates confident but factually incorrect or fabricated information. Controlled through RAG, eval pipelines and guardrails.
Fine-tuning: Adapting a pre-trained LLM to a specific domain or task by training it further on curated data. Used when RAG alone doesn't achieve required accuracy.

Talk to engineering