Services
Four engagements.
One through-line.
Each engagement stands alone, and each one makes the next possible. You can stop after any stage — we'd rather you do that than carry a system you can't operate.
How to read this page
The core path is four stages. The other assets support that path.
Aiveris sells implementation work. Company Brain maps how the company actually works, Cost Clinic diagnoses AI spend, Aiveris Platform governs tool access, and the Agent Design Framework is the method we use before building agents.
Readiness → Pilot → Production → Governance
The standard sequence for teams moving from AI intent to operated systems.
Company Brain
A focused path for teams whose real operating knowledge is scattered across docs, chats, tickets, CRM, and people's heads.
Cost Clinic
A narrower entry point when the immediate problem is LLM spend, traces, and engineering waste.
Aiveris Platform
Design-partner access for teams that need governed agent/tool boundaries.
Agent Design Framework
The workflow-first design method behind agent pilots and production systems.
- Diagnostic: 2 weeks, $7.5k-$15k
- Implementation: 4-8 weeks, $25k-$75k
- Retainer only after usage proof
- Metadata-first intake before sensitive artifacts
- New hires ask the same operational questions for months
- Support, sales, or delivery escalations depend on tribal memory
- Compliance evidence exists but is painful to reconstruct
- Founder or COO is still the routing layer for normal work
- Bad fit: you only want a generic chatbot over documents
Stage 01 · 2 weeks · fixed fee
Readiness & opportunity map
We interview 8–12 people across your business, shadow the workflows they describe, and map each one against what frontier models are actually good at today. The output is a ranked opportunity list with effort, risk, and expected payback — plus a concrete pilot plan.
Outputs
- Opportunity map (12–18 workflows, scored)
- 3–5 pilot candidates with ROI modeling
- Pilot plan: scope, eval set, exit criteria
- Executive briefing deck
- You've had 2+ AI "demos" that didn't go anywhere
- Leadership wants a defensible plan, not a pitch
- You need a pilot that survives a CFO review
- Regulated industry, or soon-to-be
Stage 02 · 6–8 weeks
Measured pilot
One workflow, instrumented properly. We build the evaluation harness before we touch the model — accuracy against a gold set your experts validate, cost per run, reviewer time, and the exit criteria that determine whether it goes to production.
Outputs
- Evaluation harness with versioned test set
- Baseline (human) vs. model performance
- Cost-per-run economics
- Go/no-go recommendation with data
- Accuracy vs. expert-labeled ground truth
- Time saved, measured against current SOP
- Failure modes — categorized, not hidden
- Cost envelope including review overhead
Stage 03 · 8–12 weeks
Production system
We build the pilot as a real internal system: auth, role-based access, full audit logs, human-in-the-loop gates, cost caps, fallbacks, and observability. Delivered with runbooks your engineers can operate — we're not trying to be a dependency.
Outputs
- Production-grade system in your cloud
- SSO, RBAC, audit trail, retention policy
- Observability dashboards + alerting
- Runbooks + operator training
- Model providers: Anthropic, OpenAI, Azure, Bedrock
- Cloud: AWS, GCP, Azure, on-prem where needed
- Auth: your existing IdP (Okta, Entra, etc.)
- Data: stays where your policy says it stays
Stage 04 · Ongoing
Governance & scale
Once you have one system shipped, the question becomes: how do we do this repeatably, safely, and at a pace the board can see? We build the program — AI policy, model-choice rubric, review cadence, and the portfolio view that keeps every workflow in flight legible.
Outputs
- AI usage policy, reviewed by counsel
- Model-choice rubric (when to use what)
- Quarterly portfolio review, written for the board
- New-workflow intake & triage process
- Financial services — KYC/AML, credit memos, research
- Legal — contract triage, discovery, memo drafting
- Professional services — proposal drafting, research synthesis
The agent control plane for teams that need AI workers inside real operating boundaries.
Aiveris Platform sits between tool-calling AI and the systems it wants to use. Claude Code, Cursor, internal agents, and MCP clients connect through one governed gateway where security teams can enforce policy before execution and produce evidence after every decision.
- Who it is for: CISOs, platform engineering, legal/compliance, and AI program owners deploying agents beyond demos.
- What it prevents: unscoped tool access, prompt-injected tool descriptions, accidental production changes, unmanaged credentials, and invisible agent activity.
- Current proof: a working Go gateway with MCP stdio/SSE transports, Cedar policy evaluation, OIDC/JWKS identity binding, Slack approval callbacks, S3/Postgres audit sinks, RFC 8693 token exchange, metrics, rate limiting, and compliance export tooling under automated tests.
- How design partners start: one risky agent workflow, one set of upstream tools, clear policy gates, and an audit trail your security team can inspect.
Trusted MCP gateway
Route approved MCP servers, SaaS APIs, repos, and internal tools through one boundary your team controls.
Identity-bound policy
Evaluate requests by user, agent, tool namespace, environment, data class, and action risk.
Human approval gates
Let low-risk actions proceed while routing destructive, expensive, or regulated actions to named reviewers.
Sanitized tool surface
Limit what agents can see and call, with safer tool descriptions and namespaces that reduce prompt-injection blast radius.
Map the agent boundary
Pick the workflow, upstream systems, identities, permissions, and failure modes worth controlling first.
Install policy + approvals
Configure gateway routes, namespaces, approval rules, logging, redaction, and evidence export.
Expand safely
Add more agents and tools behind the same control plane instead of repeating one-off integrations.
Let's talk
Start with a 30-minute call.
We'll walk through one workflow you've been trying to improve, and leave you with a concrete view of what a pilot would look like — whether you hire us or not.