SOLUTIONS / DEMAND-DRIVEN ENGINEERING
Hard technical problems.
Engineered into production systems.
General Informatics solves complex problems at the intersection of AI, data, integration, automation, security and high-performance software.
public demand signal↓problem classification↓solution cluster↓authoritative GI content↓expert contribution → qualified inboundPROBLEM FINDER
Describe the problem in your own words.
No product terminology required. Briefly say what fails today or what you want to achieve.
PROBLEM CLUSTERS
Start from the failure mode, not from a product category.
Each solution page describes causes, production architecture, failure modes, verification and security boundaries. No invented case studies or benchmark claims.
Make SharePoint AI search fast without breaking permissions.
AI search over SharePoint often looks convincing in a prototype and becomes slow, stale or unsafe in production. Large tenants combine many sites, document versions, permission boundaries and continuously changing content; retrieval must respect all of them on every query.
Open engineering note →“The RAG demo works. How do we make it reliable in production?”Production RAG is a retrieval system with evidence, not a prompt around a vector database.
The hard part of production RAG is keeping retrieval correct, authorized, reproducible and measurable while sources, embeddings, prompts and models change. A useful architecture treats indexing, retrieval, ranking and generation as separately versioned systems.
Open engineering note →“Can AI do this itself without getting full access to everything?”Treat AI agents as machine actors with explicit identity and capabilities.
An agent that can call tools is an actor in the security model. Giving it a broad API key or inheriting a human session makes authorization ambiguous, difficult to revoke and hard to audit.
Open engineering note →“It works in the demo — but will it work in daily operations?”Govern agents as operational systems, not as prompts.
Agent sprawl creates unowned machine actors, unclear dependencies and capabilities that remain active after their original use case changes. Governance must cover runtime behavior and lifecycle, not only model selection.
Open engineering note →“It works in the demo — but will it work in daily operations?”Add AI around legacy systems without pretending the legacy system is stateless.
Legacy applications often encode critical business rules in database procedures, batch jobs, UI workflows and undocumented side effects. AI integration fails when those constraints are hidden behind a thin tool wrapper.
Open engineering note →“It works in the demo — but will it work in daily operations?”HTTP 200 is not a successful business transaction.
ERP automation changes business state. A successful API response may only mean the request was accepted; posting, approval, inventory allocation or downstream accounting can still fail later.
Open engineering note →“It works in the demo — but will it work in daily operations?”Invoice automation becomes reliable when extraction and posting are separate trust boundaries.
Invoices arrive in heterogeneous formats and contain ambiguous supplier, tax, line-item and purchase-order data. An AI extraction result should be treated as evidence with confidence and provenance, not as permission to post a financial transaction.
Open engineering note →“It works in the demo — but will it work in daily operations?”Legal document intelligence needs evidence, isolation and reproducibility at collection scale.
Collections from 100,000 to multiple millions of documents combine scans, emails, office files, duplicates, versions and matter-specific access controls. The engineering challenge is not merely semantic search: every result needs defensible provenance, controlled access and measurable retrieval behavior.
Open engineering note →“It works in the demo — but will it work in daily operations?”An agent audit trail must explain what changed, who authorized it and how the final state was verified.
Traditional application logs are often insufficient for agentic systems because one user request can trigger planning, policy decisions, multiple tool calls and asynchronous state changes.
Open engineering note →“Our information is spread across SharePoint, tickets, CRM, wikis and PDFs — how can AI use it together?”Enterprise knowledge retrieval is a source, permission and evidence problem before it is an AI problem.
Useful knowledge is fragmented across document stores, wikis, ticket systems, databases and line-of-business applications. A unified answer layer must preserve source authority, permissions and freshness while ranking across different data shapes.
Open engineering note →“We spend hours every day on status chasing, copy/paste and follow-ups.”“We spend half the day on admin.” Turn repetitive work into a controlled workflow.
Teams lose hours checking status, copying information between tools, chasing missing inputs and updating the same facts in several places. The opportunity is not “add AI”; it is to identify which decisions are deterministic, which need context and where a human must stay in control.
Open engineering note →“The data is already there — it is just spread across ten tools.”“The data is already there — it is just spread across ten tools.”
Companies often do not have a data shortage. They have a context problem: customer history is in CRM, decisions are in email, documents are in SharePoint, work is in tickets and operational truth lives elsewhere. AI only becomes useful when those boundaries are connected without losing authority, freshness or permissions.
Open engineering note →“The demo works. Will it still run every day when nobody is watching?”“Can this run every day without someone watching it?”
A workflow that succeeds while an engineer watches the demo is not yet automation. Daily operation means missing inputs, expired credentials, changed APIs, ambiguous cases, retries and partial side effects must be expected rather than treated as surprises.
Open engineering note →“Why are we still chasing status and updating tickets by hand?”“Why are we still chasing status, updating tickets and copying meeting outcomes by hand?”
Project teams often spend disproportionate time collecting status, cleaning tickets, finding missing information, writing handoffs and synchronizing the same context across tools. Much of that work can be assisted or automated if ownership and source-of-truth rules remain explicit.
Open engineering note →“We know we should use AI — but where does it actually create value?”“We know we should use AI — but where does it actually create value?”
The useful starting point is rarely a model or vendor. It is a recurring delay, expensive handoff, information bottleneck or decision that can be observed and measured. Some problems need AI; others need a clean API, better data flow or a deterministic rule.
Open engineering note →“We want automation, but some decisions still need a person.”“We want automation, but some decisions still need a person.”
Human-in-the-loop should not mean a person clicks approve on everything. The engineering problem is to identify where consequences, ambiguity or policy require judgment and let routine, verifiable work continue automatically.
Open engineering note →“Can we use AI without sending sensitive data everywhere?”“Can we use AI here without sending sensitive data everywhere?”
Sensitive workflows do not necessarily rule out AI, but they change the architecture. Data minimization, execution location, provider boundaries, retention, model access and audit evidence must be explicit rather than hidden behind a generic SaaS integration.
Open engineering note →“Do we really want to automate every click in a process we already dislike?”“Do we really want AI to copy the process we already dislike?”
Automating every existing click can preserve unnecessary handoffs, duplicate data entry and approval rituals. AI creates more value when the workflow is reduced to decisions, evidence, authoritative state changes and genuine exceptions.
Open engineering note →