In progress
LLM Cost Benchmark
Five production-shaped workloads across the major model providers, scored on cost per 1,000 tasks, p95 latency and accuracy, with the harness public.
Monthly, once live
AI reliability engineering
AI for production.
Tested offline, scored online, gated in CI, built to hold.
Built for reliability: offline evals before every deploy, online evals on live traffic, and regression gates that stop a bad prompt or model swap from shipping. If it got worse this week, you know before your customers do.
ObservabilityDay one
- Eval pass rate
- Cost per 1,000 tasks
- p95 latency
- Escalation rate
- Answer drift
Every system we ship comes with this dashboard, an eval suite, and a named owner inside your team.
Reference architecture
Knowledge base
Inbound ticket
Triage agent
Resolved or escalated
Order system
Inbound ticket
Triage agent
Knowledge base
Order system
Resolved or escalated
Every ticket classified, answered from source, and logged with a confidence score.
The problem
The technology was never
the hard part.
Companies have already tried AI. The question in 2026 is not whether it works, it is why the pilot never made it to production. Seven reasons account for almost all of it.
No business objective
The pilot was scoped as a capability, not a number.
We agree the metric, baseline, and target before a line of code.
No evaluation
Nobody can say whether it got better or worse this week.
A graded eval set per use case, run on every change, gating deploys.
Poor data access
The model can't reach the system that holds the answer.
RAG over your own corpus, plus typed integrations into your CRM, ERP, warehouse, and internal APIs.
No governance
Nobody can say what the agent is allowed to touch, or what it did last week.
Scoped tool access, policy guardrails, PII redaction, and an audit trail for every action.
No monitoring
Quality drifts silently until a customer finds it.
Traces, cost, latency, and quality scores on one dashboard from day one.
No ownership
It shipped, the champion moved teams, it rotted.
Runbooks, on-call handover, and a named owner inside your org.
No ROI measurement
It works, and finance still can't justify the renewal.
A monthly report tying system output to the number we agreed in week one.
Diagnostic
Where are you today?
Six levels between no AI and an AI-native operation. Four questions place you on the ladder and name the next move. It takes twenty seconds and runs in your browser: your answers are not stored or sent anywhere.
0 of 4 answered
- L0
No AI
Nothing in production. Possibly some individual ChatGPT use.
- L1
AI Assistants
Staff use general tools ad hoc. No company data, no governance.
- L2
Workflow Automation
AI runs inside defined workflows with a human approving each step.
- L3
AI Employees
Agents own end-to-end processes and are measured like a team member.
- L4
AI Teams
Multi-agent orchestration across departments, coordinating on shared state.
- L5
AI-Native
New processes are designed for agents first, humans on exception.
Answer the four questions to see where you sit, and the single next move from there. Everything runs in your browser; nothing is sent.
Where the value is
Same architecture, different department
The engineering is largely shared. What changes is the process you point it at, and the number you hold it to. These are the numbers we would agree with each department before building anything.
No figures here on purpose. Baselines are yours, measured in week one. Targets are agreed in writing before we build.
Metric 01
Cost per resolved ticket
Baseline from your ticket volumes and support payroll, before the build.
- Baseline
- Yours, week one
- Target
- Agreed at scoping
Metric 02
Median time to resolution
Ticket open to close, on the same queue before and after.
- Baseline
- Yours, week one
- Target
- Agreed at scoping
Metric 03
Escalation accuracy
Share of escalations a human agrees needed one, graded on a weekly sample.
- Baseline
- Yours, week one
- Target
- Agreed at scoping
Capabilities
What we engineer
Seven things, each shipped with evaluation, observability, governance, and a named owner. No pilots that live forever in a sandbox.
AI Workforce
AI agents, tool calling, task queues
Agents that execute repetitive work end to end, measured on throughput and error rate like any other team.
Knowledge Systems
RAG, vector databases, hybrid search, reranking
Your contracts, SOPs, and tickets turned into a source of truth. Retrieval over your own corpus, with a citation on every answer.
AI Operations
Multi-agent orchestration, durable workflows, human-in-the-loop
Long-running processes that span your existing tools. Agents hand work to each other over shared state, with retries, checkpoints, and a full trace of who did what.
Evaluation
Graded eval sets, LLM-as-judge, regression suites, CI gates
Measure quality before your customers do. Every change scored against a set you own, including retrieval accuracy and agent task completion.
Cost Optimization
AI gateway, model routing, semantic caching, distillation
One gateway in front of every model, so you can route by cost and capability, cache what repeats, and swap providers without touching application code.
Governance and Compliance
Policy guardrails, PII redaction, audit trails, access control
Rules on what each agent may read and act on, sensitive data stripped before it leaves your boundary, and an audit log an auditor can actually read.
Enterprise Integration
APIs, SSO, VPC deployment, private networking
Connect AI to the CRM, ERP, and internal services you already run, inside your security boundary.
Sizing
What is repetition costing you?
A rough ceiling, calculated in the open. Three inputs, one number, and every assumption on screen.
Addressable annual cost
$341,250
This is the payroll cost sitting inside repetitive work that a production AI system can take over. It is the ceiling, not a promise. What you actually capture is what we agree in step 02.
Assumes 35% of that time is automatable and an 8-hour workday. We run this properly against your real process data.
Check this ceiling against your real process.
Talk through this numberResearch
What we’re measuring, in the open
None of this is published yet. Here is the programme and where each study stands. Each one ships with its methodology and data, run on production-shaped workloads rather than demos, and gets its own page when it does.
In progress
Agent Failure Report
Production-shaped agents, single and multi-agent, run many times each with every failure categorised. Failure data is rarely published; this will be.
Annual
Planned
RAG Benchmark
Chunking, embedding, vector database and reranking choices scored on the same corpus. Boring, useful, and checkable.
Quarterly
Planned
Gateway Routing Study
What routing across models through an AI gateway saves once quality is held fixed and cache hits are counted honestly.
Quarterly
Planned
Enterprise AI Maturity Index
Where companies sit on the maturity ladder, by industry and size, from results people choose to submit. The diagnostic on this site sends nothing; submitting will be a separate, explicit step.
Annual
Method
How we work
Six steps. The second one is the reason our systems survive the first budget review.
- 01
Discover
Map the process, find where the cost and delay actually sit.
- 02
Model ROI
Agree the metric, the baseline, and the target. In writing, before we build.
- 03
Prototype
Narrowest version that can move the number. Evaluated, not demoed.
- 04
Production
Integrations, guardrails, observability, security review, handover.
- 05
Measure
Report against the week-one number. Monthly, whether or not it flatters us.
- 06
Optimize
Cut cost, raise quality, widen scope. The system gets cheaper as it ages.
Engagements
Four ways to start
Each is a fixed scope with a written deliverable. Most teams start with the Eval Audit, because it tells you what everything after it is worth.
Eval Audit
2 weeks
For a team with an LLM feature in or near production and no quality signal it trusts.
- Error analysis on 100 to 300 of your real traces
- A failure taxonomy for your system
- A graded golden set of 50 to 200 cases, owned by you
OutcomeYou know how good the system is today, where it fails, and what to gate on.
Regression Gate
1 to 2 weeks
For teams that change prompts or models every week.
- Your eval suite wired into CI
- Pass and fail thresholds per evaluator
- A comment on every pull request with the score diff
OutcomeA bad prompt or a model swap fails the build instead of reaching users.
Pilot-to-Production Sprint
6 to 8 weeks
For a stalled pilot, or a new agent or RAG use case with a named metric.
- The metric, baseline and target written into the statement of work
- Build or hardening: retrieval, tools, guardrails, PII handling
- Offline evals and a CI regression gate
OutcomeA go or no-go at week six, judged against the number agreed in week one.
Reliability Retainer
Monthly, 3-month minimum
For systems already in production.
- The eval set refreshed monthly from production failures
- Drift alerts from online evals
- A regression run on every model upgrade
OutcomeQuality holds as models, data and traffic change underneath it.
Proof
What we commit to in writing
Not a logo wall. These are the terms in the statement of work, and they are unusual enough that most agencies will not match them.
0
Business metric agreed before we write code, in the contract
0 days
To a working, evaluated prototype against your real data
0%
Of the code, evals, and infrastructure handed to you
Case studies replace this block as engagements complete.
Stack
Boring where it counts
Model providers change every quarter. The layer underneath should not. Everything routes through one AI gateway, so you can swap a model without rewriting the system around it.
- OpenAI
- Anthropic
- AWS Bedrock
- Azure OpenAI
- LiteLLM
- vLLM
- LangGraph
- MCP
- Temporal
- Qdrant
- pgvector
- Weaviate
- Postgres
- Redis
- Ragas
- Langfuse
- OpenTelemetry
- Docker
- Kubernetes
- Terraform
- GitHub
Start here
Pick the number. We will build the system that moves it.
A 30-minute call. We map one process, size the opportunity, and tell you honestly whether AI is the right tool for it.
hello@venian.aiReply within one business day, from an engineer.
What happens next
- 01
A reply within one business day
From an engineer who would work on it, not a sales team.
- 02
A 30-minute call
We map one process, name the metric it should move, and tell you plainly whether AI is the right tool for it.
- 03
A written scope
If it is, a one-page scope: the metric, how we baseline it, the target, and which engagement fits.
- For
- Teams with an LLM feature in or near production, or a pilot that stalled, and a number it should move.
- Not for
- Chatbot-on-a-website projects, or strategy decks with no build behind them.