$500 /month
SYSTEM HEALTH
What we do
OURSERVICES
ai strategy & implementation
You Don't Need an AI Strategy. You Need AI Working by Monday
Most consultants deliver 60-page strategy documents you'll never read.
We deliver working tools in 8 weeks.
01
Process overview
Key Deliverables:
Technology assessment (reality, not wishful thinking)
Custom roadmap with quick wins prioritized
Working pilot in 8 weeks
6 months implementation support
12 months consultation access
Timeline: 3-6 months
Investment: $25K-$75K depending on scope
Week 9-24:
We stay until it works everywhere it should. Training your team. Deploying firm-wide. Handling resistance. Solving problems. Optimizing performance. Because implementation is where most consultants disappear.
Brutal Honesty: Some businesses aren't ready for this. If your team can't handle 8 weeks of change, or your leadership can't make decisions faster than quarterly planning cycles, we'll tell you in the first conversation. This isn't for everyone.
Week 1-2
We tell you things you don't want to hear. Your technology stack is worse than you think. Your processes have inefficiencies you've normalized. Your team has workarounds for workarounds. We document all of it. No sugar-coating.
Week 3-4
We show you what's actually possible. Not theoretical capabilities. Not enterprise solutions you can't afford. Specific tools your business could deploy in 90 days. With clear ROI. With realistic costs. With honest timelines.
Week 5-8
We build something that works. One or two pilot implementations. Real tools. Real usage. Real results. Could be automated visualization. Streamlining documentation. Process optimization. Data extraction. Whatever creates immediate value for your specific situation.
Week 9-24:
We stay until it works everywhere it should. Training your team. Deploying firm-wide. Handling resistance. Solving problems. Optimizing performance. Because implementation is where most consultants disappear.
Assessment
Strategy
Roadmap
Implementation
COMPUTATIONAL DESIGN
Parametric modeling. Generative design. Performance optimization.
From custom design systems to structural optimization to space planning and everything in between.
We solve problems that need algorithms, not conventional tools.
02
APPLICATIONS
Timeline:4-12 weeks per project
Investment:$15K-$50K depending on complexity
What This Requires
Clear problem definition. Access to relevant data. Willingness to test and iterate. If you're not sure this is what you need, schedule a call. We'll tell you honestly
Brutal Honesty: Some businesses aren't ready for this. If your team can't handle 8 weeks of change, or your leadership can't make decisions faster than quarterly planning cycles, we'll tell you in the first conversation. This isn't for everyone.
- Parametric Design
- Structural optimization
- Generative space planning
- Performance analysis
- Digital fabrication workflows
- Custom tool development
What You Get:
Not generic solutions. Custom implementations for your specific problem. Built to integrate with your existing workflows. Documented so your team can maintain and extend them.
Example Scenarios:
- You need to evaluate 10,000 layout configurations in 4 hours instead of 4 weeks
- You need to optimize structure for both cost and performance simultaneously
- You need to generate fabrication files directly from design models
- You need custom tools that don't exist off-the-shelf
Parametric
Optimization
GenAI
Interoperability
DATA OPTIMIZATION
YOUR DATA IS WORTH MILLIONS. YOU'RE USING 10% OF IT
Custom scripts. Automated documentation. Data extraction. Coordination workflows.
Most firms use their tools like expensive calculators. We extract the intelligence
03
FOCUS AREAS
- Automated documentation workflows
- Custom tools and scripts
- Data extraction and analysis
- Coordination automation
- Quality control systems
- Intelligence extraction from existing models
Example Outcomes:
We tell you things you don't want to hear. Your technology stack is worse than you think. Your processes have inefficiencies you've normalized. Your team has workarounds for workarounds. We document all of it. No sugar-coating.
Example Outcomes:
Some of the most common use cases include the development of documentation frameworks that updates automatically when design changes, cost estimation tools that pull from model data in real-time, quality control automations that catches errors before humans review for validation, and automating report generation from project data
Timeline:
6-10 weeks depending on project
Investment:
$15K-$50K depending on scope.
What This Means:
Your team already creates models, documents, databases, and files. That data contains patterns, relationships, and intelligence you're ignoring because extracting it manually would take longer than it's worth.
We automate the extraction. We build the queries. We create the tools that make
Data Encoding
Feature Extraction
Machine Learning
Neural Networks
GraphML
DATA INTELLIGENCE
EXTERNAL DATA IS ONLY USEFUL WHEN SOMEONE CAN ACTUALLY MAKE SENSE OF IT
Managing thousands of discovery documents that are never fully reviewed, sitting on vendor records, regulatory filings and market data that is never mined for patterns is no longer sustainable for any business.
04
APPLICATIONS
Timeline:6-10 weeks per project
Investment:$30K-$80K depending on complexity
What This Requires
External data is not a resource until someone makes it operational. We build custom analytical pipelines that validate, structure, and extract decision-grade intelligence from third-party data at scale, regardless of industry, discipline, or data type.
Focus Areas:
Legal e-discovery and document intelligenceClassify, cluster, and surface relevant documents from massive discovery sets. Identify patterns, inconsistencies, and key evidence without reading every file by hand.
Financial due diligence and investment analysisIngest and validate vendor records, financial filings, and market data. Extract the signals that matter for capital decisions and build audit-ready outputs.
Project and construction record analysisMine historical project data for cost benchmarks, performance patterns, and process failures. Turn past work into operational intelligence for future decisions.
Data integrity validation and QAAudit third-party datasets before you build on them. Identify gaps, duplicates, inconsistencies, and fabricated records. Clean data before the decisions, not after.
What You Get:
- Data audit and quality assessment
A clear map of what you actually have, its condition, and where the gaps are. Before any tool runs.
- Validation and remediation pipeline
Automated cleaning, deduplication, standardization, and enrichment built for your specific data source.
- Custom extraction and classification engine
Tools that surface what matters: evidence, anomalies, patterns, or risks, depending on your use case.
- Decision-support interface
A working interface your team uses to query, review, and act on findings. No raw outputs, no data science degree required.
- Source documentation and audit trail
Every finding is traceable. Every output is defensible. Every data source is documented.
Brutal Honesty:
If your external data is a mess, we'll tell you in week one. We audit before we build, every time. Cleaning bad data adds cost. Building analytical systems on bad data is a different problem entirely, one that doesn't surface until a decision goes wrong. Some clients come to us with data that isn't fixable on a reasonable timeline or budget. We say that clearly. Most clients find the audit alone is worth the engagement.
E-Discovery
Due Diligence
Validation
Classification
ai GOVERNANCE FRAMEWORK
Your team is already using AI. Is your organization covered?
AI is already in use across every industry. Staff are using general-purpose tools on client matters, patient data, financial records, and proprietary documents. Most organizations have no policy governing it, no training reinforcing it, and no accountability enforcing it.
05
SERVICE overview
Key information:
AI governance is not legal paperwork. It is the operating system for how your organization builds, deploys, and is accountable for AI. We build governance frameworks that people can easily understand and follow: clear roles, testable standards, training with teeth, and accountability that means something when something goes wrong.
Timeline: 4-8 weeks
Investment: $15K-$45K depending on organization size and complexity
Focus Areas:
AI use policy and ethical standards
Written policies that define what AI tools are permitted, for what tasks, under what conditions, and with what required human oversight. Industry-specific and actually enforceable.
Regulatory and compliance mapping
Map your AI activity against applicable regulation: professional ethics opinions, data protection law, sector-specific rules, and emerging AI legislation. Know exactly where you are exposed before a regulator does.
Tool vetting and vendor assessment
A repeatable protocol for evaluating any AI tool before it touches client, patient, or proprietary data. Data handling, confidentiality safeguards, model provenance, and liability terms, assessed and documented.
Staff training and competency certification
Training that explains why the rules exist, not just what they are. Competency assessments that document that your team knows how to use AI appropriately. Records that hold up under scrutiny.
What you get:
- AI capability and risk auditData audit and quality assessment
A clear inventory of where AI is in use, what risks those uses carry, and where the governance gaps are. Before any policy is written.
- Written governance framework
Policy, roles, approval workflows, and accountability structures. Readable by staff. Defensible to regulators.
- Tool vetting protocol and vendor assessment template
A reusable system for evaluating any new AI tool before it enters your environment.
- Training and certification program
Role-specific training and documented competency assessments. Your team knows why, not just what.
- Client and stakeholder disclosure framework
Consent language, engagement letter templates, and disclosure standards for your client-facing documents.
- Governance review process
A structured cadence for auditing framework adherence and updating policy as regulation and tooling evolve.
Policy
Compliance
Risk
Training
Ethics
Certification
How we work –
How we work –
Week 1:
Audit
Real assessment. What you actually have, not what you wish you had. We'll identify problems you didn't know existed and confirm ones you suspected. No sugar-coating.
01
weeks 2-3:
strategy
Custom roadmap. Quick wins prioritized. Clear timeline. Honest cost estimates. ROI projections based on your actual numbers, not industry averages.
02
Week 4-12:
deployment
Working tools. Not documentation about future tools. Actual implementations your team starts using immediately. Training happens during deployment, not after.
03
weeks 13 + :
optimization
Ongoing support. Monthly reviews. Continuous improvement. Because requirements change. Technology changes. Your business changes. We adapt.
04
Case Studies
CASE STUDY · LITIGATION FIRM · AUSTIN, TX
What AI looks like inside a litigation practice.
A 30-year-old Austin litigation firm, with a track record of eight-figure personal injury verdicts and a full commercial docket, came to us with a familiar problem: their attorneys were managing paperwork instead of preparing for trial.
The Challenge:Medical record review
30 to 40 paralegal hours per case, just to organize, read, and summarize records for the damages argument. On a high-volume PI docket, that adds up fast.
Deposition preparation
Attorneys reading full transcripts by hand to find key admissions and inconsistencies. Days of work before every trial, for every deponent.
Legal research load
Associates spending 15 to 20 hours per week on case law research, work that consumed expensive attorney time without requiring their judgment.
No AI governance in place
No written AI policy. Partners were aware of the Texas State Bar’s evolving ethics guidance and wanted to stay ahead of it, not scramble when it became mandatory.
What We Built in 10 Weeks:
Not generic solutions. Custom implementations for your specific problem. Built to integrate with your existing workflows. Documented so your team can maintain and extend them.
Example Scenarios:
- AI Readiness Assessment
We mapped the full workflow: every task consuming attorney and paralegal time. We identified what required a law license and what didn’t. That gap is where AI earns its keep.
- Targeted Tool Deployment
We configured a medical records analyzer that reads and flags records by damages category, a deposition intelligence tool that extracts key admissions from transcripts, and a legal research assistant calibrated to Texas personal injury and commercial case law. Each tool integrated into how the firm already worked.
- Governance Framework
We drafted the firm’s AI use policy, a tool vetting protocol aligned with Texas Professional Ethics Opinion 705, and client-facing language that protects confidentiality. Partners signed off on the framework before any tool touched a client file.
The Outcome
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Medical record review per case, down from 35 hours
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Full deposition prep, down from 3 to 4 days of manual reviewment Rate
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First meeting to fully deployed, policy signed, team trained
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AI policy on file before any tool touched a client matter
CASE STUDY · DESIGN FIRM · AUSTIN, TX
What AI looks like inside a full-service architecture practice.
A 25-year-old Austin architecture firm, with a portfolio spanning commercial, residential, and institutional projects, came to us with a problem that had nothing to do with design: their architects were buried in documentation, research, and coordination instead of building.A 30-year-old Austin litigation firm, with a track record of eight-figure personal injury verdicts and a full commercial docket, came to us with a familiar problem: their attorneys were managing paperwork instead of preparing for trial.
The Challenge:RFI and submittal managementProject architects averaging 12 to 15 hours per week drafting RFI responses and reviewing submittals. On multi-project workloads, that left almost no time for design decisions that actually required their expertise.
Building code and zoning researchEvery new project required 20 to 30 hours of code research: IBC, local amendments, ADA, energy compliance. Junior staff did the work, but senior architects had to verify everything by hand.
Proposal and fee developmentPrincipals writing new project proposals largely from scratch, re-explaining the firm's process and re-pricing similar scopes every time. No consistent structure, no reusable intelligence from past work.
No AI policy or guardrailsStaff were already using AI tools informally, with no guidance on what was appropriate on client projects. The firm needed a clear framework before exposure became a problem.
What We Built in 10 Weeks:
Not generic solutions. Custom implementations for your specific problem. Built to integrate with your existing workflows. Documented so your team can maintain and extend them.
Example Scenarios:
- AI Readiness Assessment
We mapped the full project lifecycle: from kickoff through construction administration. We separated the work that required a licensed architect's judgment from the work that was just consuming their time. That gap is where we focused.
- Targeted Tool Deployment
We configured an RFI drafting assistant trained on the firm's project specs and standard responses, a code research tool calibrated to Texas jurisdictions and the firm's common project types, and a proposal intelligence system that pulls from past scopes, fees, and project descriptions to generate first drafts in minutes. Each tool fit into workflows the team already used.
- Governance Framework
We drafted the firm's AI use policy, a tool vetting protocol covering client confidentiality and professional liability, and internal guidelines for when AI output requires licensed review before it leaves the office. The framework was signed off before any tool touched a client project.
The Outcome
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Average RFI response time, down from a full day of drafting and back-and-forth
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Reduction in code research hours per project, with licensed review still in the loop
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First meeting to fully deployed ecosystem, policy signed, full office team trained
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AI policy on file before any tool touched a client project, ahead of most firms in the market
CASE STUDY · R1 RESEARCH UNIVERSITY · UNITED STATES
What AI looks like when it makes students think harder, not less.
A prestigious R1 research university, with a century-long academic legacy and a faculty roster of nationally recognized scholars, came to us with a problem that kept their provost up at night: students were using AI to skip the thinking, not sharpen it.
The Challenge:Cognitive offloading at scaleStudents across every department were using general-purpose AI to generate answers rather than develop understanding. Research showed cognitive autonomy rates as low as 42% with standard AI tools. The university was producing output, not thinkers.
Institutional knowledge walking out the door
Decades of pedagogical depth, research methodology, and discipline-specific insight lived inside individual faculty members. Retirements and departures had no capture mechanism. When a professor left, so did everything they knew about how to teach their subject.
Tutoring demand that couldn't scale
Growing enrollment and graduate program expansion meant more students needing substantive academic support than the TA and office-hours model could handle. Quality tutoring was becoming a function of which students were persistent enough to seek it out.
No framework for responsible AI use in learning
Faculty had no shared standard for when AI support was educationally appropriate and when it undermined learning outcomes. Academic integrity policy addressed plagiarism. It said nothing about dependency.
What We Built in 16 Weeks:
Not generic solutions. Custom implementations for your specific problem. Built to integrate with your existing workflows. Documented so your team can maintain and extend them.
Example Scenarios:
- AI Readiness Assessment
We mapped how students were actually using AI across departments, where faculty knowledge was most at risk of being lost, and where tutoring demand was highest. We identified the gap between AI as a shortcut and AI as a thinking partner. That gap is what MENTOR was built to close.
- MENTOR Deployment
We built and calibrated a five-agent Socratic tutoring system trained on the university's curriculum, faculty teaching methodologies, and existing course materials. A Socratic Guide Agent leads students through questions rather than answers. A Cognitive Enhancement Agent detects dependency patterns in real time and redirects. A Domain Expert Agent provides information only when the student has genuinely exhausted their own reasoning. Faculty knowledge was captured, structured, and embedded before a single student session ran.
- Governance Framework
We drafted the university's AI in learning policy: clear standards for when AI tutoring is appropriate, how cognitive autonomy is measured, and how faculty can audit system behavior. The framework was reviewed by academic leadership and the provost's office before MENTOR touched a single student interaction.
The Outcome
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Cognitive autonomy rate, more than double the 42% measured with standard AI tools
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Higher quality of student work, measured by independent process analysis across disciplines
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From first conversation to live deployment, policy signed, faculty trained, and students enrolled
We don’t sell software licenses. We build working systems and hand the keys to your team, in weeks, not quarters.
DISCOVER
IN DETAIL:
Strategic Data Optimization
Your Data Is Worth Millions. You're Using 10% of It.
Custom scripts. Automated workflows. Intelligence extraction. Making your existing tools 300% more useful without replacing them.
After Deployment support:
We stay until it works.Some clients ask us to stay longer.
Every engagement includes post-deployment support. But the organizations that get the most from AI are the ones that treat it as an operating system, not a one-time project.
Tools evolve.
Regulations shift.
Your team AND BUSINESS grows and changes.
Regulations shift.
Your team AND BUSINESS grows and changes.
A governance framework written today needs a review next year. A data pipeline built for one data source needs to adapt when that source changes. A system your team loved in week one needs recalibration after six months of real use. That is what ongoing engagement covers.
Essential
For organizations that want assurance their deployed systems are performing as intended, without ongoing optimization.Different markets. Different roles. Different firm sizes. The same problem everywhere.
- Monthly performance review
of all deployed tools and pipelines
- Issue triage and resolution
for system errors or degraded output quality
- Tool update compatibility checks
as vendors release changes
- On-call async support
for staff questions (48-hour response)
$1,500 /month
CONTINUOUS SUPPORT
Standard · Most Common
For organizations actively growing their AI usage, adding new staff, or operating in regulated environments that require documented oversight.
- Everything in System Health +
- Quarterly governance review
with updated policy documentation - New staff onboarding
to all deployed AI systems and protocols - New tool evaluation
on request, assessed against your governance framework - Optimization sessions
(1 per quarter) to improve underperforming workflows - Priority async support
(24-hour response)
$2,200 /month
STRATEGIC PARTNER
Extended
For organizations that want Archificials embedded as an ongoing strategic resource, not just a maintenance provider.
- Everything in Continuous Support +
- Monthly strategy session
on AI landscape changes relevant to your practice - Expansion planning
as your organization identifies new AI use cases - Annual governance audit
with full policy refresh and team re-certification - Same-day response
for urgent issues and regulatory inquiries
Why Each Service
Has a Long Tail
01.
AI Strategy & Implementation
New tools enter the market constantly. Your deployed stack needs evaluation as better options emerge. Your team's usage evolves in ways the initial training didn't anticipate.
02.
Computational Design & Logic
Custom algorithms need maintenance as the inputs they depend on change. Design workflows evolve. New project types surface edge cases the original build didn't cover.
03.
Data Optimization
Data structures change. Software is updated. Extraction pipelines that worked in month one drift over time. Intelligence extraction is not a one-time event.
04.
Data Intelligence & Discovery
Third-party data sources evolve in format, volume, and quality. Validation rules that covered your data in year one may not cover it in year two. Analytical pipelines need recalibration as decision criteria shift.
05.
AI Governance Framework
Regulation is moving fast. Professional ethics opinions are being updated across every licensed industry. A governance framework with no review cadence is a governance framework that ages into liability.
FAQ
1. How long does it take to implement a custom AI solution?
Depends on service and scope.
AI Strategy: 8-12 weeks from start to first deployment. Computational projects: 4-12 weeks depending on complexity. Data optimization: 6-10 weeks.
We'll tell you exactly what to expect in the first conversation. If anyone promises faster, they're either lying or not solving the full problem.
2. What type of organization is the best fit?
Any organization where speed matters more than bureaucracy.
We work with agile teams, whether that's a specialized unit within a Fortune 500 company or a high-growth independent firm.
Size matters less than your willingness to implement real operational change.
3. What are the typical costs for AI consulting and solutions?
Consulting services: $15K-$75K depending on scope and timeline. Solutions: $500-$1,500/month depending on usage and features.
We'll discuss specifics when we understand your needs. If you have a budget in mind, tell us upfront. It helps us both figure out if this makes sense
4. Do you provide on-site implementation or remote support?
Mostly remote. Occasional on-site for training and implementation when / if needed.
Based in Austin, Texas. Working with clients internationally. Time zones matter less than communication quality.
5. What support is provided after the engagement ends?
All engagements include follow-up support. Duration depends on the service.
We stay until it works. Then we stay longer to make sure it keeps working. Most clients don't need us after 6 months. Some keep us on a retainer indefinitely.
We're available if you need us. We disappear if you don't.
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