Artificial Cognition & HAI
AI systems and interfaces implementation
Use this for
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Adding AI features to neuro/psychology, research, or decision-support products and needing behavior you can inspect and test.
Designing explanations, uncertainty messages, and refusals that users can actually understand.
Building safe handoffs from AI tools to clinicians, support teams, or human reviewers.
Meeting leadership, regulatory, or partner requests for traceable decisions, audit trails, and clear system limits.
What you walk away with
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HAI Architecture Map — who decides what, when AI acts, where humans review, and what gets logged for audit trails.
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Interaction Patterns — explanation views, uncertainty display, refusal flows, and escalation paths to human support.
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Guardrail Policies — allowed/disallowed behavior, red-team test cases, crisis escalation paths, and content boundaries.
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Evaluation Harness — test framework for task success, hallucination/claim errors, refusal quality, and user satisfaction.
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Model Behavior Specification — prompts, tools, retrieval/verification steps, and edge-case test libraries.
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Shipping Documentation — model card, HAI rationale, safety memo, monitoring dashboard definitions, and product-ready copy.
Patterns we reach for
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Retrieve → Verify → Decide (RVD) — get evidence first, verify it, then make or present a claim; uncertainty triggers handoff.
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Progressive explanations — short “why this answer” first, with deeper detail available when needed.
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Uncertainty-first design — confidence/uncertainty shown clearly, including “I don’t know” states and safer defaults.
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Refusal as product behavior — clear refusals with next steps, safe alternatives, and escalation routes.
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Boundary patterns — protect personal data (personally identifiable information, PII), prefer tool-based checks over hidden memory, and document changes.
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Outcome-focused prompting — prompt and workflow design tied to the actual decision the user must make.
Quality gates
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Critical task success — target success rate defined in advance, with mitigation plans for failure cases.
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Error budget — agreed limits for hallucinations and unsupported claims, tested on offline and shadow datasets.
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Refusal quality — high correct-refusal rates for risky/disallowed requests, with false refusals tracked.
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Uncertainty calibration — uncertainty scores within target and shown in the user interface (UI) and logs.
Latency targets — response-time targets (for example, 95th percentile / p95) by task, with fallback behavior defined.
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Traceability — inputs, sources, outputs, and human actions can be reviewed quickly during audits or incidents.
Rapid · 2–3 weeks
HAI framework & guardrails
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Risk map, task map, RVD flow, refusal/escalation logic, and explanation wireframes
Red-team test set and first evaluation pass (hallucinations, refusals, latency)
Decision memo: ship, sandbox, or fix — with clear next steps
Build · 6–8 weeks
Pilot HAI (integrated)
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Implement interaction patterns (web/mobile/desktop), retrieval/verification steps, and monitors
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Model card, safety memo, dashboard specification, and A/B test-ready copy
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Usability and model-behavior readout with targets met (or gaps clearly documented)
Oversight (Monthly)
Evidence-in-use
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Incident and refusal review, drift checks, calibration checks, test-set refresh, and change-control notes
User experience (UX) and prompt/tool updates tied to metrics, with stop-ship triggers maintained
Example runs
Eligibility assistant with rules-based screening, citations, uncertainty gating, and human handoff
Clinician support interface with explanations and uncertainty calibration for electroencephalography (EEG), heart rate variability (HRV), and electrodermal activity (EDA) signals
Adverse event triage helper that detects risk language, avoids diagnosis claims, and escalates to safe channels
Boundaries
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We do not build unsupervised diagnosis systems.
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We do not operate crisis lines; we design escalation flows, but your clinical/safety team runs operations.
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Privacy and data retention rules are built into the design (including PII boundaries).
Why Work with Us
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Verifiable track record — Project experience that can be discussed and evidenced where appropriate.
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Free consultation and progress tracking — We can talk by phone and/or video call at the start and during the project.
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Clear fees — Pricing is based on project scope and task complexity, with hourly or fixed-fee options, milestone structures, and a pre-agreed maximum number of hours per task.
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NDA agreements on request — Confidentiality can be formalized if needed.
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No prepayments — Invoices are sent only after the agreed task is submitted and approved.
Turn ideas into results that travel.
Book a 15-minute consultation or ask for a sample.
FAQ
Which models do you support?
Modern LLMs and classic ML stacks; retrieval-first designs; we use your infra or help you choose.
Do you fine-tune the model or just the UX?
Both where needed, but interaction & evaluation lead. Heavy modeling lives under AI, Modeling & Data Science.
How do you measure “hallucination”?
Task-specific claim checks (sources/ground truth), offline red-team sets, and shadow runs in production.
Multilingual ready?
Yes—copy variants, comprehension checks, and locale-specific refusal/explanation patterns.
Will this satisfy regulators?
We align to your claims and produce the traceability reviewers expect; formal submissions sit with Regulatory & Clinical Evidence.
Need Some Help?
Feel free to contact us for any inquiry or book a free consultation.
