Triuna Labs
Consulting · example engagement stories

Start with the problem.

For teams moving AI into dependable operations: assess the system, document what matters, and implement agreed improvements. Explore nine example challenges to see proposed work and possible deliverables. These are sample scopes, not completed client case studies.

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A map of production pressures

Click a problem to inspect its approach and proposed evidence. The elbowed lines group related challenges; they do not claim measured dependencies.

Select a challenge to see proposed work and possible deliverables.

Targeted corpus evaluation and prompt packs can support a risk or compliance review when useful. They are an optional method within a broader engagement.

Three example paths

What an engagement could actually answer

These are three starting points within the nine-story map. Each shows the decision, inputs, work and handoff; select a path to inspect it.

Starting points

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Decision this supports

What we examine

    Work to be scoped

      Possible outputs

        01 / 03

        These paths describe possible scopes, not completed client cases or guaranteed outcomes. The exact work, access and acceptance criteria belong in an agreed statement of work.

        For enterprise buyers

        The questions that arrive before kickoff

        Procurement, security, legal and the sponsoring team often ask different versions of the same questions. The required paperwork depends on the buyer and the data involved.

        The short answer

        An engagement can be scoped as executive assessment, technical and process documentation, hands-on implementation, or a combination. A proposal defines the systems in scope, named outputs, milestones, acceptance criteria, access, responsibilities and handoff. Targeted corpus evaluation or prompt packs are optional tools for a specific risk or compliance question.

        01 / Capability and proposal

        What exactly would you do?

        What problem will you solve, who will do the work, how long will it take, and how will we judge completion?

        Use a bounded proposal: current state, decision to support, method, deliverables, schedule, assumptions, client dependencies and acceptance criteria. Separate assessment from optional documentation and implementation.

        Typical request: RFI, RFP or RFQ; capability statement; proposal; statement of work.

        02 / Evidence and team

        Why should we trust the approach?

        What relevant experience, work samples, methods and named personnel support the proposal?

        Show the evaluation method, proposed artifacts and roles. Share client examples or references only when permission exists. These story cards describe proposed work, not verified case results.

        Typical request: qualifications, personnel bios, work samples, references and methodology.

        03 / Security and AI risk

        What will touch our data and systems?

        Which data, environments, models, tools and third parties are involved; who approves access and reviews output?

        Agree the data flow and permitted tools before access. Document access, retention, deletion, human review, escalation and any production change path in the buyer's required review. Start with the security and vendor information.

        Typical request: vendor security questionnaire, AI risk assessment or AI-CAIQ, data-flow details and a DPA when applicable.

        04 / Contracting and onboarding

        How is the engagement governed?

        What are the fee basis, IP and deliverable ownership, confidentiality, change control, insurance and support terms?

        Set these in the negotiated agreement and statement of work. Supplier setup may also ask for legal-entity, tax, payment and insurance information. Any certification or coverage claim must be verified before sharing it.

        Typical request: NDA, master services agreement, SOW, supplier form, tax form and proof of insurance as required.

        These examples reflect published procurement and vendor review materials from Stanford Procurement, Cloud Security Alliance, and Amazon Business. They are examples, not a universal requirement list.

        Read all nine story samples in text

        Consulting stories

        Browse all nine challenges with proposed work, possible deliverables and outcome focus.

        01. “Our demo is impressive, but we don’t trust it in production.”

        The prototype works most of the time, but failures are hard to reproduce, quality is subjective, and every release feels risky.

        Proposed work

        • Versioned eval definitions and scorecards
        • Golden + production regression cases
        • Deterministic tests separated from quality evals
        • Release gates with evidence, not opinion

        Possible deliverables

        • Before/after quality baseline
        • Failure taxonomy
        • Replayable regression suite
        • Release-readiness dashboard

        Outcome focus: more confidence shipping, fewer regressions, and clearer evidence of quality.

        02. “We have agents, but the workflow is becoming impossible to reason about.”

        Agents retry, call tools, route work and mutate state—but the decision logic is scattered across prompts, code and framework branches.

        Proposed work

        • Explicit contracts and context boundaries
        • Evidence-driven decision policies
        • Specialist repair agents instead of one giant agent
        • Human escalation by risk and confidence

        Possible deliverables

        • Decision trace for every run
        • Agent success + repair rates
        • Cost per accepted outcome
        • Policy versions and rollback path

        Outcome focus: safer autonomy, clearer decisions, and less operational chaos.

        03. “We cannot explain why the AI made that decision.”

        Logs exist, but they do not show the causal path from request → context → tool/model calls → eval → decision → outcome.

        Proposed work

        • Standard run envelope and event model
        • OTel-aligned traces and artifact lineage
        • Decision + override capture
        • Outcome-aware observability

        Possible deliverables

        • Readable execution graphs
        • Cost and latency by workflow
        • Human override tracking
        • Root-cause drilldowns

        Outcome focus: incidents become understandable, reviewable, and fixable.

        04. “Every AI team is rebuilding the same infrastructure.”

        Multiple products have their own prompts, retries, logging, evals, model integrations and routing logic. Velocity slows as the portfolio grows.

        Proposed work

        • Shared model/tool gateway
        • Common eval and evidence layer
        • Reusable agent capabilities
        • Product-specific policy over shared primitives

        Possible deliverables

        • Reference platform architecture
        • Migration map
        • Shared primitive catalog
        • 90-day implementation sequence

        Outcome focus: faster launches, stronger consistency, and more leverage per team.

        05. “The model bill is growing faster than the value.”

        Expensive models are used for tasks that could be solved with rules, validators, cheap classifiers, caching or confidence-based escalation.

        Proposed work

        • Deterministic-first task split
        • Cheap-model triage and extraction
        • Confidence/risk escalation
        • Prompt and context caching strategy

        Possible deliverables

        • Cost per accepted outcome
        • Routing comparison and budget policy
        • Quality/cost frontier
        • Model usage guardrails

        Outcome focus: lower spend, better latency, and no unnecessary loss of quality.

        06. “We need governance without killing team velocity.”

        The organization needs auditable behavior and controlled changes—but doesn’t want every prompt or agent update to become bureaucracy.

        Proposed work

        • Versioned contracts and policies
        • Controlled patch workflow
        • Authority-aware changes
        • Evidence bundles for review

        Possible deliverables

        • Change lineage
        • Approval boundaries
        • Rollback-ready versions
        • Audit-friendly run records

        Outcome focus: faster responsible deployment and less fear around change.

        07. “Our documents contain the answers, but RAG still misses them.”

        Important knowledge is trapped in PDFs, reports, manuals, contracts and mixed document families. Chunking alone loses structure, relationships and evidence context.

        Proposed work

        • Document-family classification before retrieval
        • Multi-pass structure, TOC and paragraph mapping
        • Entity + relationship extraction
        • Evidence-preserving retrieval and routing

        Possible deliverables

        • Recall and precision benchmarks
        • Source-to-answer provenance
        • Failure cases turned into regressions
        • Reusable document intelligence pipeline

        Outcome focus: better retrieval, better trust, and better use of document-heavy knowledge.

        08. “Everyone is using AI. What real contribution are we actually getting?”

        Commit counts and AI usage say little about whether engineering work improved a system. Design, code, tests, reviews, documentation, releases and operations all contribute, while attribution and outcomes can remain uncertain.

        Proposed work

        • Trace work from goal through design, code, tests, review and release
        • Record rework, operational burden and handoffs
        • Compare accepted work with observed operation and business outcomes
        • Treat lines of code and AI usage as context, not person scores

        Possible deliverables

        • Work-to-outcome evidence ledger
        • Review and rework map
        • Metric and attribution limits
        • Executive decision brief on what changed and remains unknown

        Outcome focus: leaders can see which changes were accepted, deployed and observed, without overstating individual attribution.

        09. “We need a clear view of our AI systems—and documentation people can actually use.”

        Leaders need a reliable view of the whole operating system around AI: goals, people, outside processes, data, software, access, risk, cost and value. Teams need documentation, ownership and recovery paths that match real operation. The current picture is often fragmented or out of date.

        Proposed work

        • Map goals, people, workflows, dependencies, data, controls and model or tool authority
        • Check the change path, live operation, failure and recovery against existing documentation
        • Separate evidence from unknowns; prioritize gaps by impact, risk and effort
        • When useful, evaluate selected document collections for risk or compliance signals; then write or implement the agreed improvements

        Possible deliverables

        • Evidence-linked production-system assessment and decision brief
        • System and process map with a prioritized gap and owner register
        • Revised technical documentation, runbooks or an implementation plan
        • Optional corpus evaluation and evidence record

        Outcome focus: leaders can make informed decisions, and operators have trustworthy documentation and a practical path to improvement.

        Have a similar challenge?

        Tell us the system, decision, and constraints. We can define a bounded assessment, documentation effort, implementation, or combination.

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