Internal — for your team
Productivity work: an agent that investigates, retrieves, and drafts, so your team stops assembling answers by hand. It runs where the team already works.
Service
Agentic AI & RAG Applications
Inside your systems, and in front of your customers.
We build AI agents and RAG applications and integrate them into what you already run — internally, to take repetitive work off your team, or inside your product, where your customers meet them. Not chatbots bolted on the side: systems wired into your data, your rules, and your tools.
Look for the work nobody wants.
Answers spread across six systems
The information exists, but pulling it together eats half a day of someone’s time and a lot of window-switching.
Work that starts with waiting
A queue whose first hour goes on figuring out who should even look at it.
Questions asked hundreds of times
Every answer is a query over data you already own, run by hand, again and again.
Two places this pays for itself.
In your product — for your customers
A customer-facing assistant grounded in your catalog, policies, and rules — answering from your data, not from whatever the model remembers.
Incident triage at FedEx.
The real cost
A single production incident could tie up engineers across multiple time zones for two to five days — digging through dozens of systems and logs while customers waited for an answer nobody had yet.
What we built
- 1One agent with read access to every system, log and database the business runs on
- 2The investigation starts the moment an incident is opened — without anyone asking
- 3It returns the root cause, the owning team and the fix in a single answer
What it achieved
- 1Root cause in under 30 minutes, down from two to five days
- 2Customers get an answer while the incident is still open
- 3Adopted as the official triage tool for FedEx customer service
What these actually look like.
An illustrative list, not a product catalog — each of these was designed around a specific bottleneck.
Incident triage
Reads logs, metrics, and recent deploys the moment an incident opens, then pinpoints the root cause, the owning team, and the likely fix.
Agent
In-product support assistant
Answers customer questions from your policies, pricing, and per-country rules, cites what it relied on, and hands off to a human when a question falls out of scope.
RAG
Ask-your-data assistant
Turns a plain question into a query, runs it with read-only access against your data warehouse, and returns the number with an explanation.
AgentRAG
Internal knowledge search
One place to ask across the wiki, tickets, contracts, and past decisions — respecting who is allowed to see what.
RAG
Back-office workflow agent
Takes a recurring request, gathers what it needs across systems, prepares the action, and waits for human approval.
Agent
Onboarding & enablement assistant
Answers new hires or new customers from your own documentation, so the same questions aren’t answered by hand fifty times.
RAG
Five steps from bottleneck to production.
- 01
Find the real bottleneck
We follow one painful workflow end to end and time it. If an agent can’t beat that time, we say so.
- 02
Wire up read access
Logs, databases, tickets, internal APIs — everything a human would open, with read-only access.
- 03
Give it tools, not prompts
The capability lives in the tools it can call. The prompt only decides when to call them.
- 04
Evaluate it before trusting it
The same evaluation method we sell as a service, applied to the agent’s own answers.
- 05
Ship it where the work happens
Inside the tool the team already uses, triggered by the event itself. Nobody should have to remember to ask.
The questions your security team will ask.
Read-only by default
Agents investigate and report. Write access is added deliberately, per action — or never.
Every step is auditable
What it queried, what came back, and how it reasoned — inspectable after the fact, not a black box.
Evaluated like any release
It ships against a scored dataset, and any regression blocks deployment.
A human stays in the loop
For anything with real impact, the agent proposes and a human confirms.
What does your team do by hand every week?
Describe the workflow — and we’ll tell you honestly whether an agent would help.
Get in touchBefore you ask.
What’s the difference between an agent and RAG — and which do we need?
RAG answers a question from your documents and data. An agent decides the next step, calls tools, and can chain several steps to reach an answer. Most real systems use both: retrieval for grounding, agency for action. We decide what you need from the workflow itself, not from the buzzword of the moment.
What access does the agent actually need?
Read access, scoped to the systems that answer the question — usually logs, a few tables, and an internal API or two. We start with the narrowest scope that works and only widen it if the answers require it.
Can it change anything?
Not by default. Write access is added one action at a time, deliberately, and anything with real impact is proposed by the agent and confirmed by a human.
How do we know it isn’t making things up?
Every run is auditable — what it queried, what came back, and how it reasoned. And it ships against a scored dataset, so any drop in answer quality blocks deployment like any failing test.
Which model or platform do you build on?
Whichever your security team has approved. The capability lives in the tools the agent calls, not in the model, so switching providers later is a config change, not a rebuild.
How do we know an agent is the right fix at all?
We time the workflow by hand first. If an agent can’t clearly beat that time, we tell you and you keep your money — plenty of bottlenecks are solved by a query or a dashboard.