Diligence self-assessment
Review the system the way a technical advisor would before a fundraise, acquisition, enterprise sale, or leadership handoff.
Use this resourceBuild it right · AI and automation
AI agents people can trust with real work
Build RAG, assistants, and tool-using workflows with controlled data access, validation, evaluations, fallbacks, and human review.
You work with senior engineers throughout. Decisions stay visible, QA is part of delivery, and the code and documentation remain yours.
The engagement in one minute
Where this work usually starts
In plain terms
A convincing AI demo can still fail when the source is missing, a document is ambiguous, a tool times out, or the model returns an answer in the wrong shape.
Why the distinction mattersRead the production context
01
Zenveus treats the agent as an operational system. We design what it may know, what it may do, what requires approval, how uncertainty is handled, and how quality and cost are measured.
Scope and scrutiny
What we take responsibility for
We plan these as parts of the same product. A visible feature is not finished if permissions, failure handling, testing, support, or production operations are still unresolved.
Grounded retrieval with cited or traceable sources
Tool and API use with scoped permissions
Structured outputs with validation and business rules
Human approval for sensitive or irreversible actions
Evaluations for quality, safety, latency, and cost
Logging, fallbacks, monitoring, and operator controls
What remains with you
Concrete artifacts, not only completed tickets
The engagement leaves the product easier to operate, change, and hand to another capable team.
01Delivery artifactAgent boundary and tool map
Built to be used after handoff, not filed away.
A working artifact with decisions, assumptions, owners, and acceptance evidence your team can continue using after delivery.
02Delivery artifactEvaluation and human-review plan
Built to be used after handoff, not filed away.
A working artifact with decisions, assumptions, owners, and acceptance evidence your team can continue using after delivery.
03Delivery artifactOperations and fallback runbook
Built to be used after handoff, not filed away.
A working artifact with decisions, assumptions, owners, and acceptance evidence your team can continue using after delivery.
Straight answers
Questions we hear before work starts
01Can our data stay private?
Yes, depending on the architecture. We map data classification, storage, retrieval, model providers, retention, and deployment boundaries before selecting the stack.
02Which model or framework should we use?
We select from the required quality, context, latency, privacy, tool use, operational maturity, and cost. The system should not depend on a fashionable framework without a migration path.
03How do you test non-deterministic output?
We combine representative evaluation sets, deterministic validation, source checks, business rules, human review, production feedback, and regression thresholds.
04How are hallucinations handled?
The system constrains what can be answered, requires evidence where appropriate, validates structured output, exposes uncertainty, and escalates when evidence is insufficient.
Delivery, made visible
What working together looks like
01
Start with the outcome, then look at the product
First we agree on the result that matters and the decision or deadline behind it. Then we inspect what already exists, follow the workflows that carry the most risk, and write down the assumptions that could change the plan.
02
Turn the unknowns into decisions
We document the architecture choices, dependencies, access needs, failure behavior, QA plan, and milestone boundaries. The proposal also names the people doing the work and makes ownership clear on both sides.
03
Review working software, not progress theatre
You see the product working as it develops. Every milestone comes with the testing evidence, open limitations, and decisions needed to accept it without relying on a polished status report.
04
Leave the product operable by someone else
Before launch, we settle deployment, monitoring, credentials, incident ownership, documentation, intellectual property, and what happens after release. The product should not depend on Zenveus being the only team that knows how it works.
Evidence from shipped systems
Use cases we have delivered
Governed natural-language analytics in a customer-owned cloud
Natural-language dashboards in under 60 seconds, deployed in the customer AWS account with zero data egress.
Cited regulatory knowledge retrieval
35,643 regulatory rules from 1,994 sources in a searchable system with cited plain-language answers.
AI-assisted clinical documentation
Workflow-specific clinical documentation using AWS Bedrock, vector search, custom knowledge, and EMR integration.
Commercial clarity
Scope the decision before the commitment
Timing and price follow the product evidence, critical workflows, dependencies, and acceptance criteria—not an attractive guess.
01Engagement
What affects timeline and cost
A discovery and architecture phase defines the workflow, data boundary, evaluation method, tools, model options, and operating cost. Delivery is then phased from a narrow end-to-end workflow to production controls and broader coverage.
We quote after we understand the outcome, the current product, the workflows that cannot fail, and the outside dependencies. That keeps an attractive opening estimate from turning into a trail of change requests. Defined work can use fixed milestones. A product that will keep changing is usually better served by a named ongoing team.
Access, accountability, and handoff
A clear boundary on both sides
01Access model
Who does the work, what access is needed, and what you keep
Private knowledge can be reviewed through sanitized samples, a controlled environment, client-owned cloud infrastructure, or narrowly scoped access. The architecture records where data travels and who can inspect it.
Review safeguards and access options
The proposal names the implementation team and the people responsible for technical review, QA, and delivery. Before work begins, both sides agree on repositories, environments, credentials, documentation, ownership, and the eventual handoff.
An honest boundary
When we would recommend something else
A clear no is useful
Agentic AI is not appropriate when deterministic rules solve the problem more safely and cheaply, when no usable source data exists, or when the organization cannot define who is accountable for AI-assisted decisions.
Your next decision
Get a recommendation you can act on
Show us what exists, where it is getting stuck, and which customer, release, or business decision is next. We will tell you whether the sensible next step is an audit, a defined sprint, an ongoing team, or something else.
Book directlyChoose a time with the engineering team
Loading available times…
Calendar not loading? Open the booking calendar in a new tab.