Business questions first
IMPAKT anchors the work to decisions, operating models, adoption, and value—not technical novelty alone.
About IMPAKT
IMPAKT translates AI infrastructure into decisions a business can examine.
IMPAKT is an evidence-led practice for the business of local intelligence: where private or local AI should run, how prototypes become governed systems, and what completed workflows cost.
Named author
Edgar Domínguez Llanos
Edgar Domínguez Llanos is the named author of IMPAKT's public field notes and video briefings. IMPAKT remains the publisher and the public identity for its frameworks, editorial standards, and decision-support work.
His published work focuses on private and hybrid workload placement, governed production agents, inference economics, and the evidence needed to move from a demonstration to an accountable operating decision.
Operator-translator
Why these questions define IMPAKT's work
IMPAKT compounds business thinking, innovation practice, enterprise delivery, and hands-on AI systems. Business discipline informs the questions, technical building supplies the evidence, and delivery work exposes the constraints that a diagram can hide.
Delivery under constraints
Cross-functional work in regulated and operationally demanding settings reinforces a practical rule: a persuasive idea is only the start. Ownership, review, handoffs, and exceptions determine whether it can be used.
Enterprise AI execution
IMPAKT's work spans interfaces, backend services, automation, evaluations, cloud infrastructure, and enterprise review. That breadth connects implementation choices to executive consequences.
Independent technical evidence
IMPAKT runs and studies private, OpenAI-compatible language-model systems, serving patterns, caches, and agent-tool integrations. Each result remains workload-specific evidence, not a universal benchmark.
Recurring points of view
Positions IMPAKT expects the evidence to keep testing
These are working positions, not articles of faith. IMPAKT revises them when a workload, measurement, or operating constraint supplies a reason.
- 01Local AI is a strategic option, not a religion. A managed API may be the better answer.
- 02The unit of value is the acceptable completed workflow, not the token.
- 03The difficult part of enterprise AI begins after the demonstration.
- 04Governance is part of architecture, not a review layer added at the end.
- 05A benchmark needs a workload, units, comparison basis, evidence date, and boundary.
- 06Organizations should decide which parts of intelligence they actually need to own.
Working method01 02 03 04 05
Turn mechanisms into bounded decision support
Frame the decision
Name the decision owner, alternatives, deadline, and consequence of being wrong.
Define the workload
Make data, volume, quality, latency, exception handling, and operating context explicit.
Separate evidence
Label what is observed, what is inferred, and what remains unknown or needs validation.
Compare the system
Assess model, infrastructure, workflow, controls, labor, and adoption as one operating system.
State the rule and boundary
Leave a usable decision rule and say where it should not be applied.
Boundaries
What IMPAKT does not claim
IMPAKT does not treat local deployment as the answer to every workload, present experiments as proof of production scale, or promise compliance, security, performance, or returns. IMPAKT does not publish confidential employer, client, protocol, or security details.
StandardsNext actions