Model selection
Reproducible comparisons across providers and open models, on your use cases — not on a paid benchmark.
Slash AI Lab
The lab behind every decision
We evaluate models and architectures with client data —in Spanish, French and English— before committing cost or trust. What fails evaluation doesn't ship.
Models OpenAI · Anthropic · Gemini · DeepSeek · GLM · Qwen · Mistral · open sourceAxes coding · reasoning · language · agents · costRule Evaluation, not hype
In short
Slash AI Lab is Slash Digital Lab's evaluation pillar: we test models —OpenAI, Anthropic, Gemini, DeepSeek, GLM, Qwen, Mistral and local open source— on the client's real prompts before committing cost or private data.
Lab rule: no model is chosen by hype. It's chosen by evaluation on the client's prompts, cost, data residency and observed failures.
What we deliver
What the lab evaluates
Evaluation axes: business Spanish, French, code, agents, cost, latency and privacy.
Language quality
Your operation's Spanish and French are evaluated as hard as English; nuances matter.
Agents & tooling
MCP, function calling, memory and human oversight: we measure what breaks when the agent errs.
Cost, latency, privacy
Inference budget, p95 response time and data residency: cloud, VPC or local inference.
How we work
How an evaluation runs
Collection
We gather real or representative prompts and success criteria with your team.
Runs
Candidates run in cloud or local; we measure quality, cost and latency.
Report
Results per axis with examples: a clear, reproducible recommendation.
Watch
The market moves every quarter; when it does, we re-run.
0fixed prompts per evaluation
0languages evaluated: ES · EN · FR
0hype: only reproducible decisions
0evaluation axes
Scope and deliverables
The method, in writing
| Evaluation | 30 fixed prompts, 5 engines, 3 languages (methodology published in /insights/). |
|---|---|
| Data residency | Standard cloud, VPC or local inference depending on compliance. |
| Report | Comparison per axis: language, code, agents, cost, latency, failures. |
| Reproducibility | Prompts, versions and criteria documented to repeat the run. |
| Cadence | Quarterly re-evaluation or on relevant market moves. |
| Publication | Findings open in /insights/ and /research/; client prompts never. |
Frequently asked questions
The questions we hear often
Which models do you evaluate?
The ones that can earn a slot: OpenAI, Anthropic, Gemini, DeepSeek, GLM, Qwen, Mistral and local open source. The list moves with the market.
Do you publish results?
Yes: method and findings go to /insights/ and /research/. Client prompts are never published.
Is going through the lab mandatory?
Recommended, not mandatory. For sensitive-data or minority-language projects, evaluation avoids months of drift.
Do you offer local inference?
Yes: open models in your VPC or own hardware when compliance requires it.
Can we evaluate a single use case?
Yes: one focused run with your prompts and a reproducible report.
Related capabilities
Where to next
AI
Artificial intelligence
Applied AI for real operations: agents, RAG, vision, voice and automation.
Learn more→ Fine-tuningFine-tuning & RAG
When to tune the model and when to give it knowledge. Judgement, not hype.
Learn more→ AgentsAI agents
Agents with tools, memory and human oversight: support, ops and back office.
Learn more→ ModelosHow we choose a model
Evaluation in Spanish and French. Production criteria.
Learn more→
Talk to Slash
Let's put it in production
Tell us your challenge. We reply within 24 hours with an honest first read: if we can help, we'll say how; if not, we'll say who can.
I reply personally. No endless forms, no canned replies.