A technical architecture brief for MarketMind's application to the AWS Activate startup program. MarketMind is a domain-agnostic intelligence infrastructure platform — designed to serve any application vertical with the same API layer, autonomous agent network, and compounding memory. This brief covers the AWS service stack, AI infrastructure via Amazon Bedrock, deployment model, and partnership opportunity.
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. All designed to run on a unified AWS infrastructure anchored by Amazon Bedrock.
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. Runs on AWS Lambda, ECS Fargate, Kong Gateway, and Amazon Bedrock via LiteLLM Proxy. No per-industry model retraining required.
Isolated tenant data, documents, permissions, and integrations. Each customer operates in a private schema within Aurora PostgreSQL. No cross-tenant data access is possible at the query layer.
Per-vertical configuration stored in DynamoDB: 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 in Aurora PostgreSQL with pgvector for vector retrieval. Optional anonymized, permissioned aggregate performance feedback provides global learning signals that improve the shared core — without sharing raw customer data. Amazon OpenSearch Service is a documented scale-up path for high-throughput retrieval as corpus volume grows.
MarketMind's technical stack spans six core layers, each mapped to a specific AWS service.
| AWS Service | Role in MarketMind |
|---|---|
| Kong Gateway | Self-managed API gateway, auth, rate limiting, quota enforcement (on ECS Fargate) |
| LiteLLM Proxy | LLM + embedding model routing, fallback, cost metering, response caching |
| AWS WAF | Web application firewall, abuse protection |
| AWS Lambda | Serverless API handlers + lightweight agent workers |
| Amazon ECS (Fargate) | Containerized long-running inference + heavy agent tasks |
| Amazon SQS | Durable agent job queue, dead-letter, retry orchestration |
| Amazon Kinesis Data Streams | Real-time multi-stream event ingestion |
| Amazon Kinesis Data Firehose | Stream archival to S3 for reprocessing |
| Amazon EventBridge | Serverless event bus for business/lifecycle events |
| Amazon Bedrock (Claude 3.5 Sonnet) | Primary LLM reasoning, structured output, tool use |
| Amazon Bedrock (Titan Embeddings V2) | Context embeddings for memory layer |
| Amazon Aurora (pgvector) | Relational store + vector memory per tenant, serverless v2 |
| Amazon OpenSearch Service (scale-up) | High-throughput k-NN vector retrieval — deferred until corpus size or query volume demands it |
| Amazon DynamoDB | Domain Pack metadata, tenant config, hot-path state |
| Amazon S3 | Raw stream archive, document storage, static assets |
| Amazon ElastiCache (Redis) | Rate-limit counters, hot data cache, session state, LLM response cache |
| AWS Secrets Manager | API credentials, service secrets, encryption keys |
| Amazon CloudWatch + X-Ray | Observability, distributed tracing, SLA tracking |
| Amazon ECR + AWS CodePipeline | Container registry + CI/CD deployment pipeline |
Amazon Bedrock provides access to Anthropic's Claude models with AWS's enterprise data commitments — no customer data used for model training, private API endpoints, and full AWS compliance coverage (SOC 2, GDPR, HIPAA-eligible). Claude 3.5 Sonnet's structured output, extended context window, and tool-use performance make it the highest-fidelity model for MarketMind's regulated-industry use cases. All domains — finance, legal, manufacturing, logistics — share the same Bedrock deployment. Domain Packs configure prompting and tooling per vertical 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.
AWS Lambda handles sub-second API invocations at near-zero idle cost, while ECS Fargate handles longer-running inference and agent tasks without managing servers. Both scale to zero when idle. SQS-driven worker scaling means agent compute expands and contracts precisely with workload volume — a critical property for a startup where usage patterns are unpredictable. The hybrid model avoids the over-provisioning cost of always-on container fleets.
Amazon Kinesis Data Streams ingests any real-time signal at scale — financial market feeds, IoT telemetry, legal filings, logistics events. SQS provides durable, decoupled job dispatch for the agent worker fleet. Domain Pack configurations define consumer group mappings per vertical, allowing the same infrastructure to serve all domains without per-vertical pipeline duplication. Kinesis Firehose archives all raw streams to S3, enabling historical replay and retroactive Domain Pack training.
AWS Activate provides up to $100,000 in AWS credits for eligible startups, plus AWS Technical Account Manager support, AWS re:Start and re:Invent access, and co-sell opportunities through AWS Marketplace. MarketMind's target customers — financial services, legal, enterprise logistics — are among the highest-density AWS enterprise verticals, creating direct alignment between the platform's go-to-market motion and AWS's existing customer base.
MarketMind AI, Inc. is currently in private early access. Target verticals have been identified across fintech, legal tech, logistics, and regulated professional services. Prototype implementations have demonstrated that the same API layer and reasoning architecture can support multiple vertical workflows without modification to the core platform.
The platform is at the stage where infrastructure investment directly accelerates customer acquisition: each new Domain Pack activated opens a new vertical to market within weeks, not months. AWS Activate credits would immediately reduce the cost of running production workloads for early API customers — particularly Amazon Bedrock inference and Kinesis ingestion, which represent the highest variable cost lines at scale.
MarketMind's use of Amazon Bedrock is structural, not incidental. The enterprise data handling guarantees, Claude 3.5 Sonnet's tool-use and structured output capabilities, and Bedrock's compliance coverage (SOC 2, HIPAA-eligible, GDPR) are core to the platform's reliability proposition for regulated industry clients — financial services, legal, healthcare-adjacent. Amazon Bedrock is planned as the primary reasoning layer for MarketMind because it provides enterprise-grade security, compliance coverage, and access to leading foundation models through a unified API.
An AWS partnership creates a natural co-sell motion: enterprise buyers already in the AWS ecosystem can procure MarketMind directly via AWS Marketplace, eliminating procurement friction for the platform's highest-value potential customers. MarketMind's target verticals — financial services, legal, enterprise logistics — represent three of AWS's highest-revenue enterprise market segments.
Questions about this brief? info@marketmindai.cloud