Small Language Models
Fine-tuned SLMs deliver frontier-level accuracy on your domain at a fraction of the cost and latency — and they run privately on modest hardware.
// custom ai & integrations
Custom AI You Own And Control
Bespoke Small Language Models and LLM integrations with guardrails and governance, wired into your stack and deployed where you need them — you own the IP.
// Trusted by
// what we build
Custom AI Capabilities
Bespoke models and a coherent architecture — accuracy per pound, private by design.
Unified AI Architecture
One coherent stack — adapters, guardrails, retrieval and APIs — rather than a tangle of disconnected tools. Built to extend as your needs grow.
Governance & Compliance
Explainability, decision logging and human oversight aligned to the EU AI Act, with the documentation you need to register and defend the system.
Deep Integrations
Your model wired directly into the software you already run, exposed through clean, secured APIs your engineers can build on.
// why stargit
Why Teams Choose Us
An AI asset that's genuinely yours: private, portable and governed.
You Own The IP
The model, its weights, the fine-tuning artefacts and the integration code are yours under contract — no rented black box, no lock-in.
Private By Design
Training data is used solely to build your model, never shared or public ones. Isolated environment, encryption and PII redaction throughout.
Deploy Anywhere
Fully on-premise, in your private cloud (AWS, Azure, GCP) or managed by us — SLMs in particular run cost-effectively on modest hardware.
// why stargit
Where Others Stop, Stargit Continues
Features
Traditional Agencies
Freelancers
At Stargit
AI-First Logic
Limited AI usage
Rarely AI-based
Fully AI-embedded
Development Speed
Structured but slow
Inconsistent timelines
Rapid agile cycles
Scalable Tech Stack
Legacy-heavy systems
Short-term builds
Built for scale (Next.js/AWS)
Communication
Account managers only
Solo execution
Direct founder access
System Delivery
Deadline-driven releases
Quality varies
Production-ready & tested
Long-term Support
Contract-based support
Limited availability
Continuous optimization
// how we work
From Data To Deployed Model
01
Assess & Benchmark
We profile your use case and benchmark SLM vs LLM to find the best accuracy-per-pound — often a hybrid.
02
Fine-Tune & Build
We fine-tune models to your tone, terminology and rules, and assemble retrieval and adapters into one stack.
03
Guardrails & Governance
Explainability, validation, decision logging and human-oversight controls aligned to the EU AI Act.
04
Integrate & Deploy
Clean, secured APIs into your software, deployed on-premise or in your private cloud with monitoring.
// our toolkit
The Custom AI Stack
L01Models5 tools
Small Language ModelsFine-TuningOpenAIAnthropicOpen-Weight LLMs
L02Retrieval & Data4 tools
RAGVector SearchEmbeddingsPostgreSQL
L03Deployment4 tools
On-PremiseAWS / Azure / GCPVPCDocker
L04Governance4 tools
ExplainabilityDecision LoggingEU AI ActPII Redaction
// proof, not promises
Applied AI, In Production
From deterministic, auditable AI engines to domain-specific assistants, see how we ship custom AI into real products.
// knowledge base
Custom AI Questions
You do. The custom model, its weights where applicable, the fine-tuning artefacts, the prompts and the integration code are all yours under contract. We build it as your asset — not a rented black box you can never leave. There is no lock-in to a proprietary Stargit platform.
Your data is used solely to build your model and never to train shared or public models. Fine-tuning and retrieval run in an isolated environment, data is encrypted in transit and at rest, and we support PII redaction and data-residency requirements. You can request full deletion of training artefacts at any point.
Yes. We deploy three ways: fully on-premise inside your own data centre for maximum control, in your private cloud (AWS, Azure or GCP) within your VPC, or as a managed hosted service. Small Language Models in particular run cost-effectively on modest on-premise hardware, which is often the deciding factor for regulated industries.
For a focused domain task, a fine-tuned Small Language Model (SLM) is often faster, dramatically cheaper to run, and easier to host privately — while matching or beating a general-purpose giant on your specific use case. We benchmark both and recommend the architecture that gives you the best accuracy-per-pound, frequently a hybrid of the two.
Governance is built in, not bolted on. We add explainability layers, decision logging, bias evaluation and human-oversight controls aligned with the EU AI Act's risk-based requirements. You get the documentation and audit trail needed to classify, register and defend your system.
// next steps
Build The Future
Join the teams who trust Stargit to engineer their most critical software.