Generic AI hallucinates in your domain
A general-purpose model doesn't know your internal regulations, your DOF provisions, or the technical nomenclature of your operation. It answers confidently about what it doesn't know.
PRIVATE FINE-TUNING · LATTICE SÉEB
AI that thinks like your organization. Trained on your data. Never exposed.
Lattice Séeb SLMs are expert, fast, and lightweight models designed for agentic AI. We specialize each one in your sector terminology, regulations, and internal logic, on-premise, with no data leaving your infrastructure.
- On-premise training
- Data never exposed
- Latency <300 ms
- Ready for agentic AI
THE PROBLEM
Why generic AI fails in specialized contexts
Using a general-purpose LLM in critical business processes is not an AI solution: it's a source of operational risk.
Your data travels to third-party servers
Fine-tuning on third-party models means sending your manuals, contracts, and policies to infrastructure you don't control. That's a regulatory and reputational risk.
Massive LLMs are slow and costly for agents
An agent that needs to make 200 queries a day can't wait 4 seconds per response or exhaust tokens on context. General-purpose LLMs aren't designed for agentic AI.
Lattice SéebExpert SLMs.
Expert SLMs.
Not foundation models.
Lattice Séeb are Small Language Models distilled from Lattice Na'at (1T). With 4B–9B parameters, they are designed for one thing: executing specific industrial tasks with speed and precision within agentic workflows.
Fine-tuning specializes one of these SLMs with your proprietary corpus(manuals, regulations, sector terminology) until the model understands your organization from the inside, not from an internet search.
- Compact: runs on standard on-premise hardware
- Fast: latency <300 ms, ideal for high-frequency agents
- Private: trained and deployed within your infrastructure
- Specialized: >94% accuracy in your specific domain
What documentation is used for training?
- Operational manuals and internal procedures
- Sector regulations (DOF, CNBV, CRE, COFEPRIS)
- Technical terminology and industry glossaries
- Standard contracts, clauses, and case law
- Clinical protocols and medical practice guidelines
- Historical reports, audits, and resolutions
Recommended minimum: 50,000 curated tokens (~100 pages). We have data augmentation techniques for reduced corpora.
PROCESS
From your corpus to an expert agent in 4 steps
A rigorous process with human validation at every stage. No shortcuts that compromise accuracy.
STEP 01
Corpus curation
We collect, clean, and structure your proprietary documentation: operational manuals, policies, sector regulations, resolutions, and terminology specific to your organization.
Deliverable: curated dataset validated by domain experts
STEP 02
Alignment and human validation
Experts in your industry validate that the corpus reflects the correct knowledge before training. We eliminate biases, inconsistencies, and sensitive data that should not enter the model.
Deliverable: approved corpus with quality labels
STEP 03
Supervised SLM training
Fine-tuning of Lattice Séeb SLMs (4B–9B parameters) on your curated corpus. Training occurs in an isolated environment (on-premise or private VPC) with no data leaving.
Deliverable: Séeb model specialized in your domain
STEP 04
Evaluation and deployment
We measure accuracy, latency, and domain coverage against real benchmarks from your operation. We deploy on your infrastructure and leave the model ready for agentic AI.
Deliverable: model in production + metrics report
COMPARISON
Generic AI vs. Specialized Séeb
The difference is not cosmetic. It's the difference between an assistant that guesses and one that knows.
| Capability | Generic LLM | Lattice Séeb |
|---|---|---|
| Accuracy in internal terminology | ~40–60% | >94% |
| Hallucinations in specialized domain | High frequency | Minimal |
| Latency per response | 3–8 seconds | <300 ms |
| Data leaves your infrastructure | Yes | Never |
| Mexico/LATAM regulatory context | Superficial | Native and deep |
| Designed for agentic AI | No | Yes (Rust + 16 layers) |
| Eligible for MX public procurement | No | Yes (CFDI 4.0 + RFC) |
| Cost per million tokens (blended) | $35–$215 MXN/M | $10–$16 MXN/M |
INDUSTRIES
Séeb already operates in regulated sectors
Each vertical has its own corpus, terminology, and regulations. Séeb is trained for each one.
Legal
Contract analysis, SCJN case law, regulatory compliance.
Financial
CNBV provisions, risk reports, KYC auditing.
Energy
CRE regulations, NOM safety, industrial asset management.
Healthcare
COFEPRIS protocols, clinical records, pharmacovigilance.
Government
DOF processes, procedures, specialized citizen services.
Manufacturing
Line manuals, quality control, OEE and maintenance.
DELIVERABLES
What you receive at the end of the process
Not just a model. A private, documented knowledge asset ready to operate.
- Specialized and validated Lattice Séeb SLM in your domain
- On-premise or private VPC deployment within your infrastructure
- Metrics report: accuracy, coverage, and latency
- Complete technical documentation of the model and corpus
- Integration ready for agentic AI with Lattice Agents
- Update and incremental retraining protocol
Total data sovereignty
The fine-tuning process, the corpus, and the resulting model live exclusively in your infrastructure. Sintérgica does not retain, copy, or have subsequent access to the trained model. It's yours.
- Zero-retention certificate at project closure
- LFPDPPP compliance throughout the process
- Compatible with corporate security policies
FAQ
Questions about private fine-tuning
No. The entire fine-tuning process occurs in your infrastructure (on-premise) or in an isolated private VPC within your current cloud provider. None of your data passes through Sintérgica's or third-party servers. Full LFPDPPP compliance.
It depends on the volume and quality of your corpus. A standard project with an already structured corpus can be completed in 3 to 5 weeks. The curation and human validation phase is the most variable. We give you a precise estimate after the initial diagnosis.
RAG searches for documents in real time and injects them as context, useful for queries about changing documents. Fine-tuning modifies the model's weights so it permanently internalizes your domain: faster, more accurate, no token cost for context. For high-frequency agentic AI, fine-tuning outperforms RAG in performance and operational cost.
For solid results, we recommend at least 50,000 tokens of curated and validated text in your domain (equivalent to ~100 dense pages). We have data augmentation techniques for organizations with reduced corpora. The initial diagnosis determines the exact feasibility.
Siguiente paso
Your AI smarter than any competitor in your industry
The model that knows your company inside out. Private, fast, and ready for autonomous agents.
- Data never exposed
- On-premise or private VPC
- LFPDPPP compliance