A technical architecture brief for MarketMind's cloud infrastructure partnership with Google Cloud. MarketMind is a domain-agnostic intelligence infrastructure platform — designed to serve any application vertical with the same API layer, agent network, and compounding memory. This brief covers the backend services, data layer, AI stack, and deployment model required to run the platform at scale.
MarketMind is not a vertical product — it is the intelligence infrastructure layer that any application, in any industry, plugs into. Think of it as the reasoning and detection engine behind the application, not the application itself.
MarketMind does not require retraining a separate model for each industry. Instead, it uses a shared intelligence core with domain-specific context, tools, retrieval sources, workflow schemas, and tenant-isolated memory. Domain Packs extend the core for law, finance, veterinary, manufacturing, logistics, regulated professional workflows, and beyond — each shipped as a pre-configured module with a guided onboarding wizard and copy-paste integration snippet, reducing vertical deployment from months to days.
Domain-agnostic endpoints (/analyze, /generate-signal, /risk-assessment, /pattern-detection) that any application calls to receive instant, structured intelligence. The shared core is extended per vertical via Domain Packs — no per-industry model required.
Isolated tenant data, documents, permissions, and integrations. Each customer operates in a private namespace. Optional anonymized, permissioned aggregate performance feedback can flow back to improve the shared core while strict data isolation is preserved.
Domain Packs configure the core for specific verticals — law, finance, manufacturing, logistics, veterinary, regulated professional workflows. Private tenant memory stores isolated context. Optional global learning signals compound shared intelligence over time.
MarketMind's architecture separates shared infrastructure from tenant-specific configuration and data.
Domain-agnostic infrastructure — shared API layer, agent execution framework, and reasoning engine. No per-industry model retraining required.
Isolated tenant data, documents, permissions, and integrations. Each customer has a fully private namespace. Commingling of raw data is not possible.
Per-vertical configuration: law, finance, veterinary, manufacturing, logistics, regulated professional workflows, and more. Includes domain-specific context schemas, retrieval sources, tool definitions, and workflow templates.
Private tenant memory for isolated context. Optional anonymized, permissioned aggregate performance feedback provides global learning signals that improve the shared core — without sharing raw customer data.
MarketMind's technical stack spans six core layers, each responsible for a distinct capability.
| GCP Service | Role in MarketMind |
|---|---|
| Kong Gateway | Self-managed API gateway, auth, rate limiting, quota enforcement |
| LiteLLM Proxy | LLM + embedding model routing, fallback, cost metering, response caching |
| Apigee (enterprise option) | Managed gateway for enterprise customers requiring vendor-operated control plane |
| Cloud Run | Intelligence API handlers + agent workers |
| Cloud Tasks | Agent job queue & retry orchestration |
| Pub/Sub | Real-time market signal ingestion |
| BigQuery | Historical signal warehouse & analytics |
| Cloud SQL (Postgres) | Relational store + pgvector memory |
| Vertex AI Vector Search (scale-up) | High-throughput ANN retrieval — deferred until corpus size or query volume demands it |
| Vertex AI / Gemini | LLM reasoning & embeddings |
| Memorystore (Redis / Valkey) | Rate-limit counters, hot data cache, LLM response cache — Valkey is Google's open-source-backed option following Redis Inc.'s 2024 license change |
| Secret Manager | API credentials & service secrets |
| Cloud Logging + Monitoring | Observability & SLA tracking |
| Cloud Build + Artifact Registry | CI/CD pipeline |
The platform uses a single shared reasoning engine with domain-specific context, tools, retrieval sources, and workflow schemas — layered via Domain Packs. No per-industry model retraining required.
Every customer's data, documents, and memory are isolated in private tenant namespaces. Customers who opt in can contribute anonymized, permissioned aggregate performance signals to improve core model quality — without any commingling of raw customer data.
The continuous worker framework ingests any data stream via Pub/Sub — market feeds, IoT telemetry, logistics events, social signals, document firehoses. Domain Pack configurations define workflow subscriptions per vertical. The infrastructure is domain-neutral by design.
The same Gemini-powered reasoning engine serves every vertical — financial signals, legal risk scoring, regulated professional workflows, supply chain anomaly detection, and any future domain. Domain Packs layer context and tooling on top without forking the model. Redis-backed response caching via LiteLLM further reduces inference cost and p99 latency for repeated or near-identical queries — a meaningful cost optimization for a usage-based platform where LLM inference is the highest variable cost line.
Projected monthly Google Cloud spend for MarketMind at the pre-revenue / early-access stage. Costs are illustrative ranges based on smallest viable SKUs and light traffic — actual spend scales with tenant count and API volume.
Cannot scale to zero — runs 24/7.
Primary fixed cost; the persistent memory store.
Smallest tier for cache + rate-limit counters.
Scales to zero or covered by free tier at startup volume.
Scales to zero when idle; a few dollars at light traffic.
Negligible until meaningful traffic.
CI/CD pipeline; low cost at build frequency.
Observability within free allotment.
The line most likely to consume credits.
Reasoning cost scales directly with API call volume.
Vector generation on every memory write / query.
Vertex AI Vector Search remains a documented scale-up path rather than a day-one cost. Keeping it out of the initial deployment avoids an always-on ANN index until corpus size or query volume genuinely demands it.
The variable LLM inference line is unbounded by traffic and is the most likely to deplete credits quickly. Credit programs generally cover first-party GCP services (Cloud Run, Cloud SQL, Vertex AI) but may exclude third-party Marketplace purchases and certain support plans — confirm Vertex AI / Gemini eligibility in the specific program terms.
* Estimates are illustrative planning figures, not guaranteed costs. Verify current SKU pricing and credit program eligibility before budgeting decisions.
Questions about this brief? info@marketmindai.cloud