We build production AI — LLM apps, RAG systems, agents, intelligent automation — with eval pipelines, cost controls, and grounding from day one. Not prototypes that break in production.
Based on 100+ Reviews
Why Entalogics for AI
Most AI builds break the same way: no evals, raw prompts, runaway cost, vendor lock-in. We solve those four problems first — before writing a feature.
AI output quality is measured, not assumed. We build the eval dataset before the first prompt and gate every release on it.
Grounded answers with source attribution. Your data stays under your control; the model just retrieves and reasons over it.
Model selection matched to task. Token usage monitored. Tiered fallbacks for cheap-when-possible, expensive-when-required.
Deploy on your infrastructure if needed. No lock-in to a single provider. Self-hosted models when the contract demands.
When to use what
Three questions every founder asks before signing. Here are the answers we give — the same ones we'd give on a discovery call.
You have proprietary data the model has never seen. You need citations, freshness, or auditability. 9 out of 10 enterprise AI builds.
The behaviour can't be elicited by prompts and you have 1,000+ high-quality examples. Mostly: tone, format, domain-specific output style.
Single-turn tasks where the model already has the knowledge. Cheap to iterate, fast to ship. Often enough on its own.
Best capability per dollar at low-to-mid volume. Anthropic Claude or GPT-4-class for hard tasks; cheaper tiers for the easy 80%.
Llama / Mistral via inference providers when your volume makes API tokens expensive. Or when you need to fine-tune.
Regulated data, air-gapped deploys, or strict data-residency. We deploy on your VPC with Ollama, vLLM, or Triton.
Fuzzy inputs, language-heavy tasks, summarisation, classification with messy categories, agent-style tool use.
Deterministic logic, exact arithmetic, strict business rules, regulatory calculations. SQL and code do this better.
AI for the language layer, traditional code for the math and state. The boring answer is usually the right one.
Product shapes
The shapes of AI work we've shipped most often — 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 build — not as a sales line, as a checklist before launch.
Engagement shape
A typical AI engagement, end-to-end. Evals come before features, monitoring before scale. No demo-ware that breaks in week three.
Stack
Picked by problem, not by hype cycle. Each row below has been load-tested across real AI shipments.
ENGAGEMENT
No hourly retainer that bills for 'thinking time.' Pick a lane that matches your stage; everything is fixed-quote or transparently rated.
For founders shipping their first AI feature or product
For founders who need a working AI product — not a demo. Fixed scope, fixed quote, senior-only team. Eval pipeline included from day one.
Series A scale-up adding AI to an existing product
For teams moving faster than they can hire. Senior AI/ML engineers in your Slack, your GitHub, your standups.
Enterprise or regulated verticals
For regulated industries that can't send data to third-party APIs. Self-hosted models, data residency, full audit trail.
Founder-direct
Free 30-minute architecture call with a senior AI engineer. By the end you'll have a model recommendation, an eval plan, and a realistic ship date — whether you hire us or not.
