What We Do • Advise
Guidance you can trust.
We help ops and engineering teams make clear decisions - scope, quality, and operating plan - before they write more code.
Ops guidance grounded in real workflows.
Engineering practices your team can repeat.
Automation-first decisions. Less manual drag.
When is Advise the right choice?
Advise is for teams with real stakes - deadlines, defensibility, uptime, margin, or support load - who need clearer decisions before they write more code. We turn ambiguity into written plans and decision docs your team can execute, review, and keep using.
If AI is on the table, we treat it like any other production dependency: fit check first, measurable success checks, guardrails, and an operating plan before it reaches users.
- You're about to invest in a new capability and need build vs buy vs integrate clarity-plus scope boundaries that won't move every week.
- QC, exceptions, and manual handoffs are eating throughput. You want repeatable automation, but you need a plan operators can trust.
- Your team keeps shipping "almost done." You need a Definition of Done and acceptance checks tied to real workflows-edge cases included.
- Integrations or extensions are becoming a support problem (drift, partial failures, upgrade fragility). You need clear integration rules, a recovery plan, and clear checkpoints.
- You need decisions that stay defensible: audit trail expectations, traceability, and workflow-specific constraints that change what "correct" means.
- AI is being requested internally or externally. You need a go/no-go gate, evaluation criteria, and review paths before you bet a release on it.
Why development projects stall in regulated industries
Most failures are not coding failures. They are decision failures - early assumptions go untested and the expensive parts show up late: QC, edge cases, access, and support.
- Scope is written as features, not workflows-so exceptions, handoffs, and failure paths are missing.
- "Done" is subjective-acceptance moves, and rework becomes the default.
- Operability is postponed-monitoring, runbooks, and ownership are designed only after an incident.
- Data nuance is ignored-auditability, permissions, privilege/redactions, and retention rules change the rules of "correct."
- AI gets treated like a demo-no evaluation criteria, no guardrails, and no monitoring plan.
What does success look like?
You leave with decisions that are executable. Clear scope. Clear checks. Clear operating assumptions - so delivery stays predictable and support stays sane.
Buildable scope with boundaries
A scoped plan your team can execute: what's in, what's out, and what comes first - without hidden dependencies.
Fewer reversals
Tradeoffs and risks are written down. Decisions don't get re-litigated every sprint when priorities collide.
Run-ready operating assumptions
Ownership, monitoring expectations, and recovery steps are clear before launch - so operators are not stuck waiting on engineers.
AI stays controlled
If AI is in the workflow, success checks and guardrails are defined up front so outputs are measurable, reviewable, and traceable.
Advise deliverables
Outputs are written to be executed - no slideware. Use them to run an internal build, scope a project, align stakeholders, or de-risk a release before it becomes a support problem.
Scope shaping and sequencing
Turn requests into buildable scope with boundaries, milestones, and "what's not included."
- Build vs buy vs integrate recommendation, with the rationale behind it.
- In/out scope boundaries and a phased milestone plan.
- Dependencies, decision owners, and constraints that must be true for delivery to succeed.
- Packaging and support implications (what changes for operations, support, and users when this is shipped).
Workflow and QC strategy
Model how work really happens, with exceptions included - then design QC that reduces risk and manual touch time.
- Current → target workflow map with handoffs and known failure points.
- QC gates and sampling strategy tied to defensibility and throughput.
- Error classes and routing with what gets retried, what needs review, and what stops the line.
- Operator actions with what humans do, when they do it, and how recovery is handled.
Definition of Done with readiness evidence
Make "done" testable-so you don't ship demo-only behavior.
- Draft Definition of Done with reliability, auditability, and day-to-day run requirements.
- Scenario-based acceptance criteria with real inputs and edge cases.
- Validation plan and evidence checklist with what proves it works before production.
Operating plan and support model
Design what happens after launch: who owns what, how failures show up, and how triage works.
- Ownership and escalation map with support boundaries.
- Monitoring expectations with logs/metrics/alerts aligned to operator troubleshooting.
- Runbook outline with checkpoints, safe retries, rollback notes, and known failure states.
- Risk register and decision log that stays useful after the engagement ends.
AI that helps and stays trustworthy
We support AI-enabled features and workflows, but we start with the outcome and what can't go wrong. If AI is not the right fit, we'll say so. When AI is the right tool, we define success checks, guardrails, traceability, and an operating plan so it can be trusted in production.
- Fit check first: baseline approach vs AI, with a clear go/no-go gate.
- Measurable checks: evaluation criteria and acceptance tests defined before production.
- Guardrails: human review paths, constraints, and fallbacks where risk requires it.
- Traceability: logged inputs/outputs, versioning, and user actions for auditability.
- Operations plan: monitoring notes, alert routing, and a runbook.
What you receive: A short, written fit assessment with success checks, guardrails, traceability expectations, and an operating plan.
Ways to engage
Discovery
Turn uncertainty into a plan.
Includes:
- Clarify the real problem, who it's for, and what success means.
- Map workflows, data sources, constraints, and risks up front.
- Define acceptance criteria and a Definition of Done everyone agrees on.
Retainer
Stay unblocked and make better decisions.
Includes:
- Advisory on eDiscovery and legal tech workflows and best practices.
- Engineering guidance on standards, reliability, security, and integrations.
- Fast unblockers: reviews, troubleshooting, and decision calls when you're stuck.