The AI-native software aggregator.
Enterprise software built above the model market: model-agnostic, continuously benchmarked, cost-aware and governed by design. Stratinv AI Tech turns a changing supply of frontier models, open models, small models and inference providers into stable platforms, products and APIs.
Treat intelligence as a supply chain, not a supplier.
Five clean layers. Every layer replaceable.
Separation is what makes quality, resilience and model arbitrage possible. Applications request a task; the platform resolves the model, inference path, policy and price behind a stable interface.
Model layer
Frontier LLMs, open-weight models, fine-tuned SLMs, embeddings, rerankers, speech and vision.
At least two qualified suppliers per capability, all behind one internal schema.
Inference layer
Vendor APIs, specialist inference clouds, optimised serving engines, quantised deployments and batch endpoints.
Qualify the same open model on multiple serving paths; measure cost, latency and reliability weekly.
Orchestration & routing
Model gateway, task router, prompt registry, caching, fallbacks, rate and budget governors, and the evaluation harness.
Build and own this layer. It is the proprietary intelligence fabric where telemetry compounds.
Agentic layer
Planners, tool-using agents, multi-step workflows, retrieval, code sandboxes and human-approval gates.
Stateless workers operate over durable state; every consequential action is policy-checked and logged.
Application & delivery
Enterprise products, platform services, public APIs, external integrations, billing, metering and tenant administration.
Applications consume named tasks only—never model IDs, prompts or provider SDKs.
Quality first. Cheapest qualified path second.
Every workload is classified honestly. Routine work stays on efficient models; frontier capacity is reserved for tasks where it changes the outcome. A cascade evaluates low-cost output and escalates only when the quality bar is missed, while provider fallbacks protect availability.
- Typical work
- Classification, tagging, extraction, intent routing and PII detection.
- Default path
- Fine-tuned SLM, private serving or batch API.
- Escalate when
- Confidence falls below threshold or the document class is novel.
- Typical work
- Summaries, emails, report sections, FAQs and boilerplate code.
- Default path
- Mid-tier open model through the cheapest qualified provider.
- Escalate when
- An evaluator flags the output or the user rejects it.
- Typical work
- Multi-step analysis, non-trivial code, contract reasoning and planning.
- Default path
- Frontier model on a standard reasoning tier.
- Escalate when
- The case is long-horizon, specialised or high-stakes.
- Typical work
- Autonomous workflows, architecture design, and financial or legal drafting for review.
- Default path
- Best qualified frontier reasoning with a human gate.
- Escalate when
- Never automatically downgraded; correctness takes precedence over cost.
Illustrative steady-state design target: 70–85% of token volume on T1–T2 economics, with frontier spend concentrated where it changes outcomes.
Task-oriented routing
The application names the task—not the model. Routing stays dynamic, reversible and invisible above the gateway.
Private evaluation
Production-derived test suites determine whether a model, prompt or provider clears the quality bar.
Continuous repricing
Route by cost per successful task, review monthly and react immediately to major releases or price changes.
Distil the workhorse
High-volume stable tasks move toward fine-tuned SLMs; frontier models remain teachers and exception handlers.
Rent first. Own late. Attribute every request.
Inference is a second arbitrage dimension. Stratinv AI Tech begins with flexible per-use capacity, moves stable demand only when measured utilisation justifies commitment, and keeps burst capacity rented. Every request carries tenant, product, feature, task, model and provider metadata so margin is visible at the unit of value delivered.
Conservative autonomy. Observable execution.
Agentic systems concentrate enterprise value, but errors and cost compound across steps. The architecture therefore favours explicit control, recoverability and auditable consequences.
Workflows before autonomy
Use fixed, testable sequences whenever the path can be known. Open-ended agents run only where the path is genuinely uncertain—and under step, token and time budgets.
Tools as contracts
Every tool is a typed, versioned, least-privilege API with validated inputs and outputs. Payments, deletion, external communication and production change require a hard gate.
Durable state, stateless workers
Progress lives in a database, not a context window, so any step can resume, retry or move to a different model without losing control.
Full observability
Prompts, decisions, tool calls and outputs are traced and replayable for debugging, audit, evaluation and future distillation.
Platforms, solutions, APIs, integrations and billing—designed as one system.
Enterprises buy dependable outcomes rather than model access. Every product capability therefore ships with a service level, a defined failure mode, strict tenant controls and model-agnostic contracts.
Platform & product development
Multi-tenant isolation, encryption and residency options, RBAC, SSO, audit logs and named AI capabilities with explicit failure behaviour.
Task-oriented APIs
Versioned endpoints for tasks such as extract, draft and review; strict schemas, idempotency, webhooks, structured outputs, confidence and grounding metadata.
Systems integration
Event-driven connectors for ERP, CRM, ITSM, HRIS, document stores and warehouses; adapter layers, circuit breakers and qualified substitutes for dependencies.
Third-party API exposure
Standardised, governed access to external capabilities and emerging tool/context protocols, keeping products portable across providers.
Billing APIs & metering
Real-time usage events, rating, subscriptions, included allowances, outcome pricing, enterprise commitments, budget caps and margin monitoring.
Solution engineering
Outcome-led software that combines the platform, APIs and agentic workflows around a clearly measured enterprise process.
Independent controls around every intelligent system.
Quality and safety are enforced outside any single model. Controls are standing operating systems, not documents assembled when an audit begins.
Evaluation
A private, production-derived suite blocks regressions exactly as failing software tests block release.
Guardrails
Prompt-injection and exfiltration screening, schema and policy validation, PII controls, and grounding checks run independently.
Security
Least-privilege tools, sandboxed execution, isolated secrets and strict minimisation of data sent to third parties.
Data governance
Provider eligibility by data class and contract, residency-aware routing, trace retention and mechanically enforced opt-outs.
Compliance
Risk classification, model cards, decision logs and human-oversight records are maintained continuously.
Reliability
Multi-provider fallback, graceful degradation, tier-aware load shedding and scheduled provider-outage exercises.
Inherit the progress of the entire model market.
Stratinv AI Tech packages the best qualified intelligence path for each enterprise task—lowering dependency, controlling cost and improving the system through evaluation and telemetry with every request served.