Systems Architecture for Institutional AI
Most transformation firms stop at recommendations. We execute the technical architecture required to integrate AI into institutional systems—whether that work stems from our diagnostic engagements or your internal requirements.
This is not a development shop. We do not provide staff augmentation, offshore capacity, or feature-based contracting. We own architectural outcomes, not delivery tickets.
Boundaries
The MVP Mentality
We do not build prototypes for internal validation. We build systems designed for institutional durability and long-term maintenance.
Feature-Level Contracting
We do not accept engagements defined by UI/UX feature lists. We solve architectural bottlenecks and integration logic.
Static Implementation
We do not deliver code that lacks a governance framework. Every engineering output includes a technical maintenance and evolution plan.
Organizations that treat AI as a feature-set create technical debt. Institutional AI requires systems that survive team turnover and model evolution. We maintain these boundaries to protect architectural integrity.
When AI Engineering Applies
You have technical leadership but lack AI-specific architectural judgment.
Your internal teams can execute. What you need is the design logic for integrating AI into institutional systems without creating ungoverned endpoints or compliance gaps.
You require systems that survive organizational change.
This is not MVP development. AI Engineering applies when the cost of architectural failure exceeds the cost of deliberate design.
You need implementation that doesn't require continuous vendor dependency.
We build systems your teams can maintain. Transition is designed into the engagement structure, not treated as a handoff event.
Technical Capabilities
Integration Architecture
We do not "connect apps." We design the middle-tier logic that allows LLMs to interact safely with legacy databases and proprietary data silos without compromising security protocols.
- API orchestration and governance
- MCP server implementation
- Secure data pipeline design
- Legacy system modernization
Automation Engineering
This is not about script writing. We build automation infrastructure that monitors agent behavior, surfaces failure modes early, and maintains operational visibility within your existing CI/CD pipelines.
- Workflow automation architecture
- Agent monitoring systems
- Process orchestration (n8n)
- CI/CD integration logic
Platform Development
We do not build stand-alone applications. We develop the foundational platforms that allow your internal engineering teams to deploy and manage AI capabilities across multiple business units.
- Multi-tenant platform architecture
- Database design (Postgres, Prisma)
- Authentication infrastructure
- Model deployment frameworks
Engagement Model
We function as an extension of technical leadership, not additional headcount. Engagements begin with infrastructure audit to identify integration surface area, security constraints, and architectural gaps.
Unlike agencies, we do not work in isolation. We operate within your stack, using your version control and security standards, while providing the architectural judgment your internal teams may lack.
Accountability is measured by system stability, integration success, and architectural adherence. We do not report on "story points" or "velocity." We report on systemic readiness and operational resilience.
Technical Evidence
Workant represents a structural solve for workforce management. We moved beyond simple LLM wrappers to build a retrieval-augmented generation (RAG) system that prioritizes data privacy and auditability.
Status: Active development, production usage
For organizations with high-security constraints, we deploy local-first AI architectures. This removes dependency on third-party API availability and keeps proprietary data within the institutional firewall.
Deployment: On-premise, air-gapped environments
Technical FAQ
We don't "hand over" code. We build the documentation and governance frameworks alongside your team. Transition is a continuous process of technical knowledge transfer.
No. We sign SLAs for the architecture we build around those models. We design systems to be model-agnostic so your infrastructure survives vendor shifts.
Staff augmentation increases headcount. IntraWeb increases technical judgment. We reduce the long-term cost of technical debt by preventing architectural drift before it begins.
We focus on the high-friction boundaries: API orchestration, data pipeline integrity, and secure model deployment. We leave frontend UI development to your internal product teams.
No. Proof-of-concept work belongs to marketing. Our engagements require a minimum timeline sufficient to ensure the system is institutionally viable.
The organization. We build proprietary assets, not leased services. All architecture and logic remain institutional property upon completion.
Ready to discuss technical requirements?
Engineering conversations begin with specificity. Bring your constraints.
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