We build AI — LLM apps, RAG systems, AI agents, and generative AI development work — with real pipelines, cost control, and grounding from day one. Not prototypes that break the moment real users show up.
Based on 100+ Reviews
100+ Reviews
Why Entalogics for AI
Most AI app development projects break the same way: no evals, raw prompts, runaway cost, vendor lock-in. As an AI development company, we solve these four problems first — before writing a single feature.
If AI output isn't measured, it's guessed. We build the eval dataset before we release the first prompt, so cost, accuracy, and vendor tradeoffs are decisions you control — not surprises you discover in production.
Grounded answers with source citation. Your data stays in your control, and every response traces back to a document, not a hallucination.
Token spend modeled before launch. Tiered fallbacks stop cheap requests from routing to expensive models by default.
Deploy on your own infrastructure. No lock-in to a single provider — self-hosted, VPC, or hybrid, on your terms.
When to use what
These are the questions every founder asks before signing. Here are the answers we give — the same ones we'd give on a discovery call, before any formal AI consulting engagement begins.
Your knowledge base is the model's brain. Best when facts, freshness, or auditability matter — no retraining needed as your data changes.
The behaviour you want the model reinforced with 1,000+ high-quality examples. Best for tone, voice, or a specific task the model keeps getting wrong.
Single-shot tasks where the model already knows the domain. Cheap to build, quick to test, slower to scale.
Best quality-to-time ratio for early builds. Zero infrastructure to manage. Vendor risk is the tradeoff.
Llama, Mistral, or open reference models you fully control. Fine-tune freely and avoid per-token billing.
Regulated data or air-gapped environments. Deploy on your own VPC, at Azure, GCP, or bare metal.
Fuzzy inputs, language-heavy tasks, ambiguity, agent-style rule interpretation.
Deterministic logic, exact arithmetic, deterministic compliance rules, and rule-code that's easier to test.
AI at the language edge, traditional code for the math and compliance-heavy core.
What we build
The shapes of AI development work we've shipped most often — spanning generative AI development, machine learning development, and natural language processing — each with the integrations we reach for first.
Quality system
An AI product is only as good as the evals around it. Five things we do on every AI development services engagement — not as a sales line, as a checklist before launch.
Engagement shape
A typical AI development engagement, end to end. Evals come before features, monitoring comes before scale.
Stack
Picked by problem, not by hype cycle. Each row below has been load-tested across real AI development services shipments.
ENGAGEMENT
No hourly retainer that bills for 'thinking time.' Pick a lane that matches your stage — everything is fixed-scope or transparently staffed.
For founders who need a working AI product in production — not a slide deck
Hire AI developers embedded directly in your team
Hire AI developers embedded directly in your team when you need ongoing AI development services without a hiring cycle.
For regulated industries where architecture has to be right the first time
Founder-direct
Free 30-minute architecture call with a senior AI engineer. By the end of it, you'll have a model recommendation, an eval plan, and a realistic timeline — no sales pitch, just the plan.
