Solutions
Production-grade solutions that turn our open-source packages into deployable AI systems for specific business problems. Pick one, follow the DIY tutorial to see how it's done, download the examples and deploy them on your own infrastructure — for free — or tell us which ones you want customized and deployed.
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14 solutions · page 1 of 2
aws-bedrock-contract-clause-extraction-for-smb-legal
Small law firms and contract-heavy SMBs manually review every agreement to find renewal dates, liability caps, and termination clauses. Missing a deadline or misreading a clause leads to unbilled work and client disputes.Automatically extract key clauses, dates, and parties from contracts using AWS Textract and Bedrock, with structured output and cost tracking.
vllm-observability-suite-for-smb-ai-operations
Small businesses running vLLM for AI inference struggle to monitor token usage, latency, and cost across multiple agents, leading to overspend and undetected performance regressions.Prebuilt observability stack with OpenTelemetry traces and dashboards for any AI agent using vLLM as the inference backend.
cohere-llm-cost-observability-for-smb-support-agents
Small businesses running Cohere-powered support bots have no per-call cost visibility; a single verbose handling loop can silently triple the monthly bill.Wrap every Cohere API call with cost telemetry and OTel spans so SMBs can see exactly where their LLM budget goes and stop cost overruns.
ollama-agent-eval-harness-for-on-prem-smb-support-qa
SMBs running on-prem LLMs with Ollama lack automated QA to catch regressions in agent performance before customers encounter errors, leading to support drift and quality degradation.Run continuous quality evaluation on local AI agents using Ollama, with regression gating and cost tracking, all from a CLI.
databricks-llm-observability-for-smb-production-ai
Small businesses deploying Databricks-hosted LLMs lack visibility into latency, token usage, and spend across their applications, making it hard to debug slowdowns or control costs.Drop-in OpenTelemetry tracing and cost attribution for every Databricks model call, visualized in Langfuse, so small teams can monitor LLM performance without building custom instrumentation.
agnostic-discovery-doc-review-assistant
A litigation partner at a 5-attorney firm faces discovery requests on cases with modest budgets. Manual review of thousands of documents is uneconomic, often forcing the firm to settle or go pro se. The partner spends weekends reviewing docs, burning out and missing key evidence. Small matters become unprofitable due to the high cost of discovery.Make document review economic for small cases with automated summarization and privilege flagging.
perplexity-rag-eval-suite-for-smb-knowledge-bases
SMBs that deploy internal RAG bots for employee or customer support find their answers drift as documents change. Without automated evaluation, they only discover quality regressions through user complaints, with no reproducible benchmark and no way to track LLM judging costs.Continuously evaluate your small business RAG knowledge base using Perplexity’s LLM-as-judge, heuristic metrics, and cost-tracked CI gates from REAA’s eval packs.
agnostic-per-tenant-llm-cost-chargeback-2
As a product manager at a vertical SaaS company, you need to offer AI features to your SMB customers but each customer may use different LLM providers based on their plan. You lack per-tenant cost tracking, making it impossible to charge back usage accurately. This leads to margin erosion and prevents you from scaling AI features profitably. You need a solution that captures LLM costs per tenant and integrates with your existing billing system.Track and bill each SMB customer for their AI usage with granular cost attribution.
anthropic-code-sandbox-for-smb-data-cleansing-pipelines
Small businesses often need to clean and transform CSV, JSON, or database exports but lack the infrastructure to safely execute LLM-generated code. Running it directly risks data corruption, runaway costs, or exposure of sensitive records.Safely run LLM-generated data transformation code in an isolated sandbox, with cost tracking and automatic output repair.
databricks-code-sandbox-for-secure-smb-data-analysis
Small businesses with data in Databricks need ad‑hoc reports and analyses, but hiring a data engineer for every query isn’t feasible. Non‑technical staff often write inefficient or unsafe code, risking runaway costs.An AI agent that translates natural language into safe SQL and Python queries, runs them on Databricks, and returns results with cost tracking and guardrails.
xai-grok-observability-for-smb-ai-workflow-monitoring
SMBs integrating Grok into their workflows lack visibility into how often the model is called, what it costs, and when it fails. Without monitoring, they risk overspending and missing performance degradations.Single-pane monitoring for token usage, latency, errors, and cost across all Grok-powered features in your SMB app.
agnostic-per-tenant-llm-cost-chargeback
The VP of Engineering at a B2B vertical SaaS company needs to bill each SMB customer for the AI features they use. Without per-tenant cost tracking, the company absorbs all LLM expenses, eroding margins. Existing observability tools don't tie token usage to tenant IDs, making chargeback impossible. The team manually approximates costs, leading to billing disputes and lost revenue.Attribute every LLM call to the right tenant and export cost data to your billing system.