Our Solutions
Three interconnected service lines — custom SLMs, automated ML pipelines, and a 12-month evolution plan — designed to give your team sovereign, low-cost, high-precision AI.
Service Line 01
Generic large language models are trained on everything — which means they're masters of nothing. Our custom Small Language Models are purpose-built on your domain data, delivering superior accuracy at a fraction of the cost.
| Dimension | 🧠 AntimKros SLM | Frontier LLM | Open-Source LLM (Self-hosted) |
|---|---|---|---|
| Model Size | 0.5B – 7B params | 70B – 1T+ params | 7B – 70B params |
| Inference Cost | Hardware + Ops cost | $0.01–$0.06/1K tokens | Hardware + Ops cost |
| Domain Accuracy | 95–99% (targeted) | 70–85% (generic) | 60–80% (generic) |
| Data Privacy | private-network | Data leaves perimeter | On-premise possible |
| Latency (p99) | < 400ms(targeted) | 800ms – 4s | 200ms – 1.5s |
| Compliance Ready | HIPAA, GDPR, SOC2 | Vendor-dependent | Self-managed |
| Auto-Upgrade | 12-Month Pipeline ✓ | Vendor-controlled | Manual / DIY |
| Setup Timeline | 6–12 weeks | Days (API key) | 4–16 weeks |
| Vendor Lock-in | None — you own it | High | Low |
We train exclusively on your internal corpus — proprietary documents, historical records, domain ontologies, and structured data. The result is a model that speaks your industry's language natively, with no hallucinations from unrelated training data.
Fine-Tuning + RLHFOur SLMs are quantized (INT4/INT8) and pruned for edge and on-premise hardware. Runs on a single A100 or even enterprise-grade CPUs — no $500k GPU cluster required. Lower power, lower cost, same precision.
GGUF · ONNX · TensorRTThe model weights, training data, and inference endpoints live entirely within your infrastructure. We architect with zero data egress — no telemetry, no logging to external servers. Air-gapped deployment available.
Air-Gap ReadyEvery SLM we ship comes with a full benchmark report: accuracy vs. GPT-4 on your test set, latency distributions, cost-per-query analysis, and compliance attestation. You know exactly what you're getting before go-live.
Eval Report IncludedService Line 02
Beyond language models, we architect complete machine learning systems — from raw data ingestion to automated deployment — tailored to your team's workflow and your industry's compliance requirements.
// System Architecture Flow
Automated ingestion from SQL, NoSQL, data lakes, APIs, and file stores. Schema validation, PII scrubbing, and versioned datasets out of the box.
Forecasting, anomaly detection, churn prediction, and risk scoring models. Trained on your historical data with explainability layers for auditors.
Document OCR, quality inspection, medical imaging classification, and object detection pipelines — all deployable on-premise without cloud APIs.
Automated extraction, classification, and routing of contracts, invoices, medical records, and compliance documents at scale.
Private internal chatbots and voice assistants grounded in your knowledge base. No hallucinations. Full audit trail. Works offline.
Model registry, A/B testing infrastructure, drift detection dashboards, and automated retraining triggers. Your models stay healthy forever.
Service Line 03
AI models decay. Business data changes. Our automated upgrade pipeline ensures your custom SLM never stagnates — continuously improving with your operations, not against them. Zero technical debt. Zero surprises.
Phase 1
We audit your data landscape, map ingestion sources, define success metrics, and architect the training pipeline. Delivered: Data readiness report + SLM blueprint.
Phase 2
Initial SLM training on curated domain data. Rigorous evaluation against GPT-4 on your test sets. Iterative fine-tuning until accuracy targets are met.
Phase 3
On-premise deployment, API integration with your existing stack, user access controls, and monitoring dashboards. Security audit before go-live.
Phase 4
Real-time performance tracking, data drift alerts, and weekly accuracy reports. Automated triggers flag when retraining is needed before users feel the impact.
Phase 5
New data batches are ingested, the model is retrained on the expanded corpus, and v2.0 is validated in a shadow environment before silent rollout. Zero downtime.
Phase 6
Full-year performance report, ROI analysis, and a strategic AI roadmap for the next 12 months. Expand capabilities, add new use cases, or scale infrastructure.
Quarter 1
Months 1 – 3
Quarter 2
Months 4 – 6
Quarter 3
Months 7 – 9
Quarter 4
Months 10 – 12
We'll run a free model feasibility audit on your data stack and show you exactly what accuracy, latency, and cost savings are achievable — before you commit to anything.