Agentic AI consulting services

Consulting that ends with an agent running in production

Engineering-led agentic AI consulting services. The team that scopes your use case also builds, deploys and monitors the agent, and you own the code.

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Why engineering-led

Consulting done by the engineers who build the agent

Most agentic AI plans fail between the strategy document and the first release. We check your data and systems before the plan is written.

Strategy-led consultingEngineering-led, Vstorm
Scope and buildTwo teamsOne team
Data checkAfter the roadmapBefore the roadmap
End resultA documentA working agent
OwnershipVendor platformYour code, open source
2 hrs → 3 minSynera: scoped and built by the same team

How pricing and scope differ: boutique consultancies vs large firms.

When to bring us in

Four situations where agentic AI consulting pays off

Choosing the first use case
You have a long list of ideas and need to know which workflow an agent can take over first, and what it will return.
Rescuing a stalled pilot
A prototype works in a demo but not with real data, real users or real volume, and nobody can say why.
Build, buy or platform
You are choosing between a custom agent, an off-the-shelf tool and a vendor platform, and want an answer from people who have built all three kinds of system.
Scaling from one agent to several
The first agent is live and the next ones need shared foundations, governance and monitoring before they multiply the risk.
What you get

Three outputs and one decision

Each output is backed by your own data. The Proof of Value decides whether the project goes on.

  1. 01

    Ranked use cases

    Ranked by value, feasibility and the cost of an error.

  2. 02

    Architecture

    Single agent, multi-agent, retrieval or an existing product.

  3. 03

    Business case

    One success metric and the cost per task.

  4. Decision gate

    Proof of Value

    A working agent on your data. It meets the metric, or the project stops.

After the Proof of Value

Go

Roadmap and governance

The next agents, in order, and the points where a person approves or overrides.

Stop

No full build

The agent missed the metric, so the project ends before the full build.

The path follows the TriStorm methodology, the same three phases we use from the first workshop to production.

Case studies

From consulting to agents in production

Clinical triage, engineering workflow generation and investigative-journalism support, each scoped and built by the same team.

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Synera text-to-workflow agentic platform

2 hrs → 3 min to generate a validated workflow

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Schmitt-Thompson agentic RAG system for clinical triage guidelines

Schmitt-Thompson Clinical Content

44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark

ARIJ Network multilingual AI agent supporting journalist training

ARIJ Network

1% → 100% share of journalist inquiries answered, manual desk vs agent

Track record

Consulting backed by delivery

Counted deployments and people, not projections.

30 +
Production deployments since 2017
25 +
AI engineers on the team
12
Industries delivered into
Want to know which of your workflows an agent can take over?

Talk through your workflow, feasibility, and the fastest path to a working agent in production.

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FAQ

Questions about agentic AI consulting

Why do most AI agent projects fail before reaching production? +
Most agentic AI projects fail because they are run like ordinary software projects. An agent calls models, tools and company systems, so it has failure modes a web app does not: wrong tool calls, hallucinated values, silent drift after a model update. Across 30+ deployments we have seen the same pattern. The projects that ship treat the agent as an engineering system with tests, observability and guardrails from the first sprint, not as a prompt to be tuned later.
How do you bridge the gap between our AI vision and what's technically achievable? +
We check each use case against your data, your systems and your budget before anyone writes a roadmap. Then we build it on real data and stop at a Proof of Value gate: the agent either meets the agreed success metric or the project does not move on. The same engineers who scoped the work build it, so nothing is lost in a handoff between a strategy team and a delivery team.
We're a mid-size company - are AI agents only for enterprises with massive budgets? +
No. Most of our clients are mid-market companies. We start with one workflow where an agent can pay for itself, reuse components we have already built, and scale only after the first system proves its value. You own the code, so there is no platform subscription that grows with usage.
What's the difference between agentic AI and the automation tools we already use? +
Rule-based automation follows a fixed path and stops when an input does not fit it. An agentic AI system decides which step to take next: it reads context, calls tools and handles exceptions that would break a scripted workflow. That flexibility is why agents need more engineering around them, including evaluation sets, permission limits and logging of every decision.
How do you make sure an agentic AI project delivers measurable ROI? +
We agree the success metric before the build starts and instrument the system to report it from day one. Observability shows cost per task, accuracy and time saved, so the business case is measured rather than estimated. In one deployment, an agent took over work equivalent to eight full-time employees and paid back its cost within 30 days.
Which business processes benefit most from agentic AI? +
Knowledge-heavy processes where people spend their time gathering data, checking documents or moving information between systems. Typical examples are document processing, customer service, due diligence and multi-step workflows that span several tools. We rank candidate processes by volume, data quality and how costly an error would be, and start where the ratio is best.
How long does it take to deploy a working AI agent in our existing workflow? +
A single agent in an existing workflow usually takes 6 to 12 weeks. Multi-agent systems that span several departments take 3 to 6 months. You see a working prototype on your own data within the first month, and each later release adds capability to a system that is already running.
How do you scale AI agents across multiple departments without disrupting operations? +
We build a shared foundation first: model access, tool integrations, evaluation and monitoring. Each new agent reuses it, so the second and third use cases ship faster than the first. New agents run alongside existing processes until their results match the agreed quality bar, and only then take over.
Do we own the AI agents you build, or are we locked into your platform? +
You own everything. We build on open-source frameworks and hand over the code, the prompts and the evaluation sets. There are no platform fees and no proprietary runtime, so you can change model providers, extend the system or move it to another team without asking us.
What happens if our team has no AI expertise - can you still help us adopt agentic AI? +
Yes, and that is a common starting point. We run the engineering end to end and train your team as we go, with documentation and pairing on the parts they will maintain. By the end of the engagement your engineers can operate and extend the system, and you decide how much ongoing support you want from us.
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