Document intake and triage
Invoices, claims, RFPs and applications: extract, validate against your rules, route the exceptions with a reason attached and clear the rest.
Discuss this patternAgentic AI
Agents that do the work, inside a policy you set
A chatbot answers. An agent acts — it reads your systems, decides a next step, does it, and checks its own work. The difference between the two is entirely in the engineering around it.
MWRKJT
Delivery leads online — talk to a human, not a bot4Steps in the loop
EveryAction logged
HumanOn the risky calls
Operating loopObserve · plan · act · verify
100% delivery success — delivered worldwide across seven regions
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
The loop
Four steps, one policy, one accountable human
Every agent we ship runs the same loop. What changes between clients is the tools it can reach, the actions it is allowed to take without asking, and who signs off on the rest.
- Observe — reads only the systems and rows it is scoped to, with your existing permissions
- Plan — proposes a next step against a written policy, not a free-form instruction
- Act — calls a tool, writes a record, drafts a message; every call logged with its inputs
- Verify — checks its own output against rules and escalates anything it cannot confirm
Run telemetrySuccess · escalation · cost
Where it pays
Good candidates look the same everywhere
High volume, rule-heavy, currently done by people reading one screen and typing into another.
Service and support triage
Reads the ticket, retrieves the account context, drafts the reply and either sends it or queues it for a human — depending on confidence and value at risk.
Discuss this patternRecurring analysis
The report somebody rebuilds every Monday: pulls the data, checks it against last period, writes the commentary and flags what moved and why.
Discuss this patternData quality remediation
Detects a broken record, traces it to the source, applies the documented fix and files anything outside policy for review.
Discuss this patternBack-office reconciliation
Matches records across two systems that were never designed to talk, and surfaces only the residue a human needs to judge.
Discuss this patternCompliance monitoring
Reads new filings, policies or contracts against your control set and raises what changed, with the clause cited.
Discuss this patternGuardrails
The boring parts are the reason it works
Autonomy is a dial, not a switch. We start every agent at the conservative end and open it up only where the evaluation data supports it.
- Scoped credentials — the agent inherits a role, never a superuser key
- An allow-list of actions, with value thresholds above which a human approves
- Full audit log: inputs, tool calls, outputs, cost, and who approved what
- Evaluation suite run on every prompt or model change, with a pass bar
- A kill switch and a documented rollback that someone has actually tested
Control planePolicy · identity · audit
By the numbers
Where agentic work has landed
Grounded in real agentic builds — an HR-automation workflow and a multi-agent text-to-SQL platform. Supervised mode first, autonomy earned afterwards.
0Agents in our text-to-SQL pipeline: table, column and SQL generation
0Agentic builds shipped: HR automation & text-to-SQL
0%Agent actions logged and attributable
0Superuser keys — agents inherit scoped roles
Delivery model
How an agent engagement runs
Same delivery model as the rest of our work, with an extra gate before anything acts unsupervised.
01
Scope the decision
We map the process as it is done today, count the volume, and price a single completed unit of work. If the maths does not clear, we stop here.
02
Build supervised
The agent proposes; a human approves every action. This produces the training and evaluation data that everything after depends on.
03
Evaluate
We measure agreement with the human reviewer, cost per action and failure modes, and publish a pass bar you sign off.
04
Release autonomy
Low-risk, high-agreement actions run unsupervised. Everything else keeps the approval gate — permanently, if that is the right answer.
05
Operate
Monitoring, monthly evaluation runs and a change process for prompts and models, treated like any other production release.
FAQ
Questions buyers actually ask
Straight answers. If yours is not here, ask us on a call — we will tell you if we are the wrong fit.
Ask a questionA chatbot returns text. An agent takes actions in your systems — creating a record, sending a message, updating a status — which means it needs identity, permissions, logging and an approval path. That engineering is most of the work.
Actions are scoped and reversible where possible, everything is logged, and value thresholds force human approval on anything material. In supervised mode nothing happens without a person clicking approve.
We build on established orchestration frameworks and your cloud's managed model endpoints. We avoid proprietary agent platforms that would make your logic hard to move.
We track cost per completed action from the first sprint and report it alongside accuracy. If the unit economics do not beat the manual process, that is a finding, and we will tell you.
Next step
Bring us the process everyone complains about
If it is high volume, rule-heavy and currently done by someone copying between two screens, it is worth thirty minutes of conversation.