Insights · Editorial map
Four pillars. 47 topics. 5 live, 42 in the backlog. This is the content engine — not a random blog calendar.
Compliance · 1 live
How to bake HIPAA, DPDP, audit logging, and consent into the first commit — not a last-mile checklist.
ICP job: Ship regulated AI without killing product velocity
Audit logging for every inference call
Targets: How to audit log LLM API calls for HIPAA
What to log, what never to log, retention, and who can access the trail.
BAA before PHI: the sequencing most teams get wrong
Targets: When do I need a BAA for an LLM vendor?
Decision tree for vendors, subprocessors, and when synthetic data is enough.
Targets: How do you build HIPAA compliant AI products from day one?
Bake HIPAA/DPDP into the first commit; contrast checklist theater vs system design.
DPDP consent UX that founders can actually ship
Targets: DPDP consent requirements for AI products India
Practical consent, purpose limitation, and notice patterns for Indian buyers.
HIPAA vs DPDP: one product, two postures
Targets: HIPAA vs DPDP for AI startups
Shared primitives (access control, logs, minimization) vs jurisdiction-specific deltas.
Choosing an LLM vendor under regulated constraints
Targets: Best LLM providers for HIPAA healthcare
BAA availability, data retention defaults, region, and eval portability — not model leaderboard cosplay.
Data residency without freezing the roadmap
Targets: Data residency requirements for AI products India US
Region pinning, vendor contracts, and when residency is theater.
Human-in-the-loop that clinicians will use
Targets: Human in the loop design for clinical AI
Review UX, override paths, and liability-aware product design.
On-prem vs private cloud for regulated AI
Targets: Should healthcare AI run on-prem?
Decision matrix: latency, cost, talent, and actual compliance need vs fear.
PHI minimization in RAG pipelines
Targets: How to keep PHI out of LLM context windows
Chunking, redaction, retrieval filters, and eval hooks for leakage.
RBAC and break-glass for AI features
Targets: Access control for clinical AI tools
Role scopes for copilots, admin overrides, and audit of privileged prompts.
Incident response when the model is in the blast radius
Targets: LLM security incident response healthcare
Prompt injection, data exfil, wrong clinical advice — playbooks that map to existing IR.
Evals · 2 live
Treat evaluation as a first-class deliverable. Regression suites, safety dims, and shipping without demo-grade guesswork.
ICP job: Stop shipping LLM features that only look good in demos
CI that blocks deploys on failing evals
Targets: Run LLM evals in CI/CD
Sampling, cost caps, flake handling, and which suite is required vs nightly.
Targets: Why LLM evaluation suites matter for production AI
Eval suite as a scoped deliverable that changes how you negotiate engagements.
Targets: Turn production AI incidents into eval regressions
Cultural + technical loop from incident → fixture → CI gate.
Safety evals for clinical and health chatbots
Targets: How to evaluate safety of healthcare chatbots
Refusal, escalation, medical advice boundaries, hallucination of dosages/claims.
Three eval dimensions: functional, qualitative, safety
Targets: LLM evaluation dimensions for product teams
Brandlabs Discovery Sprint pattern — set targets in week 1–3.
Building a prompt regression dataset that ages well
Targets: How to build LLM regression test datasets
Golden sets, adversarial sets, and who owns updates.
Evaluating multilingual health assistants (India)
Targets: Evaluate Hindi Hinglish healthcare chatbot quality
Locale fixtures, code-switching, and safety across languages — Myna lessons.
Groundedness evals for RAG in regulated domains
Targets: How to measure RAG groundedness healthcare
Citation fidelity, refusal when retrieval is empty, and poison documents.
LLM-as-judge: when it helps and when it lies
Targets: LLM as judge evaluation reliability
Calibration, bias, and when human rubrics still win — Precision lessons.
Offline vs online evals for AI products
Targets: Offline vs online LLM evaluation
When batch suites lie; when production scoring is too slow.
Closing the loop from Learn content to eval agents
Targets: AI evaluation platform for product teams
How Precision’s Learn hub and Agent Library reinforce shipping quality — lab proof.
Eval cost budgets that don't bankrupt the sprint
Targets: Cost of running LLM evaluation suites
Sampling strategies and model tiers for judges vs product models.
Delivery · 2 live
How embedded pods actually ship zero-to-one AI in 12–20 weeks — demos, scope, and operating cadence.
ICP job: Buy a pod that ships product, not slideware
Targets: Weekly demo cadence for AI product teams
Friday demos as evidence, not theatre — what ships, what doesn't, what you say out loud.
The two-week Discovery Sprint, unbundled
Targets: AI product discovery sprint for startups
Inputs, outputs, kill criteria, and how it maps to a 12–20 week engagement.
Targets: What is a forward deployed engineer?
Not PM, not classic consultant — senior eng in your Slack writing the hard code.
Zero to PMF in 12–20 weeks: the phase model
Targets: How long to ship an AI MVP with an embedded team
Foundation → alpha → harden → PMF hunt — with eval and compliance gates.
Alpha to 10 trusted users
Targets: How to run an AI product alpha with trusted users
Recruiting, feedback loops, and what blocks production hardening.
Fixed timelines for AI work that still discovers
Targets: Fixed price AI product development risks
How Brandlabs keeps 12–20 weeks honest with kill switches and evidence demos.
Handover vs extending the embedded pod
Targets: When to keep an embedded AI team after launch
Clean handover criteria vs continued velocity needs.
Negotiating scope when evals are in the contract
Targets: Scoping AI consulting engagements with evaluation criteria
How defining eval targets changes change-orders and success criteria.
Who sits in an embedded AI pod
Targets: Embedded AI engineering team structure
FDE + 2 seniors + designer + compliance-as-needed — roles and anti-patterns.
Operating inside the client’s Slack without chaos
Targets: How forward deployed engineers work with client teams
Channels, decision logs, and async norms that keep trust high.
Rebuild components when the data demands it
Targets: When to rewrite AI product components after launch
PMF hunt phase: courage to discard vs sunk-cost theater.
Sector · 0 live
Sector-specific patterns from US healthcare and Indian regulated industries — telehealth, SRH, MSME compliance AI.
ICP job: See proof in my market and regulatory posture
AI for Indian MSME compliance (lessons from Correct)
Targets: AI compliance software for Indian MSMEs
69k+ obligations, 15 changes/day — productizing regulatory chaos without legal malpractice.
How US healthcare startups should buy AI build partners
Targets: Hiring AI engineering studio for healthcare startup
Diligence questions: BAA fluency, eval discipline, clinical workflow empathy.
Safety engines for WhatsApp health assistants
Targets: Building safe healthcare chatbots on WhatsApp
Escalation, localization, and content safety — Myna Bolo patterns.
Trust UX for telehealth and women’s health platforms
Targets: UX patterns for trusted telehealth in India
Privacy, language, and stigma-aware flows from M-Health / Qi Health work.
Voice AI for low-literacy health access
Targets: Voice AI for rural healthcare India
Latency, vernacular STT/TTS, and when voice beats chat — Myna Voice lessons.
Keeping clinicians as authors of the record
Targets: Clinical AI copilot documentation authorship
Suggest vs auto-write; audit; liability-aware defaults.
Privacy architecture for SRH products
Targets: Privacy design for sexual reproductive health apps
Stigma, shared devices, and data minimization beyond generic HIPAA talk.
Regulated-industry AI buyer’s guide (India)
Targets: AI vendors for regulated industries India DPDP
Questions CFOs and compliance officers should ask before signing.
US–India delivery for health AI products
Targets: Offshore AI engineering for US healthcare startups
How Brandlabs runs NY + Mumbai without leaking PHI or slowing compliance.
Building health platforms NGOs can run for years
Targets: Sustainable digital health platforms for NGOs India
Operability > novelty — M-Health multi-year partnership lessons.
Explainable AI at the grocery shelf
Targets: Explainable AI for food label scanning apps
SatvikScan: one-handed trust, dietary principles, verdict UX.
Stack choices for specialty telehealth (TCM case)
Targets: Building online TCM clinic platform
Intake → consult → herbal plans as one system — Qi Health.