AI agents that do real work. Not chatbots. Not dashboards. Autonomous systems that collect, analyze, decide, and report while you focus on running your business.
We design, build, and deploy multi-agent pipelines tailored to your specific workflow. Every agent has a defined role, validated output, and a guardrail keeping it honest.
Most businesses know AI could help them, but the options are either too generic or too expensive to justify. Off-the-shelf tools give you a chatbot that summarizes text. Enterprise platforms cost six figures before you see a result. Neither option solves the actual operational bottleneck you are dealing with.
ChatGPT wrappers and plug-and-play tools do not understand your industry, your competitors, or your data. They produce generic output that still requires hours of manual review.
Competitive research, reporting, data collection, and analysis tasks consume hours every week. Time your team spends on repetitive work instead of strategic decisions.
When an LLM hallucinates a pricing figure or fabricates a claim, who catches it? Most AI implementations have no validation step, no guardrail, and no audit trail.
Purpose-built agent pipelines where every stage has a defined role, validated output, and cost controls.
Agents that crawl competitor sites, extract pricing and positioning data, compare against your offerings, and deliver structured weekly briefs.
Multi-step pipelines that replace manual research, data entry, report generation, and quality checks with autonomous agent chains.
Systems that collect data from multiple sources, synthesize insights, validate claims, and generate polished reports on a schedule.
Not one model doing everything. A coordinated team of specialized agents, each with a single job.
Specialized crawlers and scrapers that discover, navigate, and extract raw data from websites, APIs, and documents.
Transform raw, unstructured data into clean, structured formats that downstream agents can reason about.
Cross-reference multiple data sources to identify patterns, gaps, and opportunities that humans would miss or take hours to find.
Validation gates that catch hallucinations, verify pricing math, flag unsourced claims, and enforce output quality before anything reaches you.
How a 10-agent pipeline replaced gut-feel pricing with data-backed strategy, automated price adjustments, and a weekly intelligence brief that runs itself.
The founder of Fox in the Sawdust, a custom woodworking studio in Rexburg, Idaho, was not waiting around for the industry to catch up. He understood that businesses willing to integrate AI into their operations today will set the terms for their markets tomorrow. His competitors had dedicated marketing teams and pricing analysts. He had something better: the clarity to recognize that a well-built system could outperform an entire department. His vision was to turn his website into a living salesman, one that could read the market, adjust pricing in real time, and close the gap between his craft and his competition without hiring a single person. He came to us with a specific mandate. Not a chatbot. Not a dashboard. A competitive intelligence engine that could crawl his market, compare real pricing across competitors, surface strategic gaps, recommend price adjustments grounded in actual data, and execute approved changes automatically.
Each agent has one job, its own model, context budget, and output schema
The comp_scout does not dump a homepage into an LLM and hope for useful output. It runs a three-phase crawl: first it fetches the competitor's root page and discovers all internal links. Then an LLM call picks up to 8 category pages most likely to contain product listings. A second call selects up to 12 individual product pages with actual dollar amounts. A third call performs the full competitive analysis. This costs more per competitor, but it means the system actually finds real pricing data instead of guessing from marketing copy. On a typical run with 3 competitors, this agent alone accounts for 9 LLM calls.
The wood_expert knows the difference between white oak and red oak, understands why a mortise-and-tenon joint commands a higher price than pocket screws, and can tell whether two tables from different makers are actually comparable. Without this agent, the system would compare a $4,000 hand-built dining table to a $200 particle-board desk and call it a pricing insight. Products are matched by category, construction quality, and a minimum similarity score of 0.5. This agent is what makes the pricing recommendations credible.
The recommender produces exactly 9 actions: 3 UX improvements, 3 pricing recommendations, and 3 quoting workflow changes. Every pricing suggestion references the owner's actual dollar figures, competitor benchmarks, and source URLs. For example: "Suggest testing: raising the Farmhouse Dining Table from $1,757 to $1,895, since comparable tables from a regional competitor start at $2,100." Each recommendation appears in the dashboard as an approvable card with impact and effort tags. Once the owner approves a price change, the system can auto-adjust it on the live site. No spreadsheets. No guesswork. Approve and it is done.
Before any report is generated, the guardrail agent runs two passes. The first is regex-based: it catches percentage guarantees, certainty claims like "will definitely increase conversions," absolute statements, and unverified proof claims. The second pass is a full LLM review against policy constraints: JSON format preserved, recommendations framed as suggestions (never commands), no unsourced pricing, and human approval required for all actions. Every flagged item is visible to the owner with a clear explanation of why it was blocked. The output is binary: passed or review-needed.
The analyst cross-references structured competitor data with the internal site evaluation and produces exactly 5 insights, each with a theme, evidence, impact rating, priority, and source URL. The key constraint: every insight must describe what the owner's site does, what competitors do differently, and what to do about it. Early versions would say "Competitor X has strong trust messaging." That is interesting but useless. The current version says "Your site has no trust block on the homepage. Competitor X has a craftsmanship guarantee above the fold. Adding a similar block would address this gap." Same data, completely different utility.
The report_gen synthesizes everything into a structured weekly intelligence brief: executive summary, strategic context, competitor overview, and the top 3 priority actions. The report renders in a custom dashboard with a dark woodshop theme, glassmorphism panels, and a live 3D brain-network visualization showing all 10 agents with real-time status coloring as they execute. Alongside the report, the Action Queue surfaces every recommendation as an approvable card. The Pricing Dashboard shows per-product cards with current vs. recommended pricing, percentage differences, source citations, and expandable rationale. Approve a price change and it pushes live.
| Agent | Role | Stage | Model | Context Cap |
|---|---|---|---|---|
own_scout | Crawl client site, discover product URLs | Collect | gpt-4o-mini | 50K |
own_extractor | Extract product listings with pricing | Extract | gpt-5.4-mini | 50K |
comp_scout | Three-phase competitor site navigation | Collect | gpt-4o-mini | 50K |
extractor | Normalize competitor data into comparison schema | Extract | gpt-5.4-mini | 2M |
wood_expert | Match comparable products by domain knowledge | Extract | gpt-5.4-mini | 2M |
site_analysis | SWOT evaluation of client site | Analyze | Global default | 50K |
analyst | Cross-reference data, produce strategic insights | Analyze | gpt-5.4-mini | 2M |
recommender | Generate approval-ready pricing and UX actions | Strategy | gpt-5.4-mini | 2M |
guardrail | Regex + LLM validation, block hallucinations | Validate | gpt-5.4-mini | 2M |
report_gen | Compile intelligence report with action queue | Output | gpt-5.4-mini | 50K |
A one-click system that delivers a complete competitive intelligence brief with pricing recommendations the owner can approve and push live. Press a button, walk away, and come back to a structured report with competitor pricing, market positioning analysis, SWOT evaluation, and 9 approval-ready actions. Approve a price change and the system adjusts it on the live site automatically. What used to be impossible for a one-person operation is now a weekly strategic advantage. The businesses building these systems today will own their markets tomorrow.
Each agent has a single job, its own model selection, context budget, and output schema. When something breaks, you know exactly which agent failed and why.
Validation is a pipeline stage, not an afterthought. Every claim is checked against regex patterns and LLM review before it reaches you. Flagged items include clear explanations.
Per-agent context caps, sliding-window rate limiters, and right-sized model selection keep API costs predictable. Scouts use lightweight models. Analysis agents get the heavier ones.
Agents suggest. Humans approve. Recommendations use hedged language by design. The system never executes changes on its own without explicit owner sign-off.
Any repetitive workflow involving research, analysis, and reporting is a candidate for agentic automation.
Automated competitor monitoring, pricing intelligence, and market positioning analysis delivered on a schedule.
Extract, classify, and route information from invoices, contracts, applications, or compliance documents.
Aggregate support tickets, reviews, and feedback into structured insights with sentiment analysis and priority scoring.
Validate data quality, check compliance, and flag anomalies across systems before they become costly problems.
Research, draft, fact-check, and format content with multi-agent pipelines that maintain your brand voice and accuracy standards.
Collect, transform, validate, and load data from multiple sources with built-in error handling and quality gates.
We map the manual process you want automated. What triggers it, what data flows through it, what decisions it produces, and where quality breaks down.
We decompose the workflow into specialized agents, define their roles, select models, set context budgets, and design the validation layer.
Each agent is built, tested individually, then integrated into the pipeline. Guardrails are tuned against real edge cases from your domain.
The system goes live with full visibility into agent execution. We monitor output quality and adjust agent prompts, models, and thresholds as needed.
A system of autonomous AI agents that work together to complete a multi-step task. Instead of one model doing everything, specialized agents handle collection, extraction, analysis, validation, and reporting.
ChatGPT is a single conversational model. Agentic systems are pipelines of multiple models, each with a defined role, validated output, and guardrails. They produce structured, repeatable results without manual prompting each time.
API costs vary by pipeline complexity. Per-agent context caps and right-sized model selection keep costs predictable. We design every system with cost control as a core constraint, not an afterthought.
Yes. Agent pipelines can pull data from APIs, databases, websites, spreadsheets, and documents. Output can be delivered as reports, structured data, or pushed to your existing systems.
Guardrail agents run regex pattern matching and LLM-based claim verification. Unsourced claims, fabricated numbers, and certainty statements are caught and flagged before output reaches you.
No. We handle the architecture, development, and deployment. You interact with the finished system through a simple interface or scheduled reports.
Tell us about the manual process that is eating your team's time. We will assess whether agentic AI is the right fit and scope the solution.