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For fintech and financial services

Agents that act on accounts and money, with every action authorized.

Built with the authorization, safe retries and audit trail financial systems require, so an agent can read accounts, prepare actions and work alongside your operations team.

What we solve

When an agent can move money, every action needs proof.

01

Every action authorized

Per-caller, per-resource checks on every tool call, with human approval wherever money or customer records move.

02

Retries that never double-post

Idempotent tool actions, so a timeout or a repeated request cannot execute the same operation twice.

03

An audit trail you can follow

Traces that connect the request, the model's reasoning, the tool call and the approval behind every outcome.

Proof

Work we have already done.

We built the agent platform behind a product used by large enterprises and a unicorn startup: 50+ MCP tools, semantic search and model integrations with Anthropic, OpenAI and Google.

AI-Native SaaS / selected work

Agent Platform Engineering for Enterprise AI

The problem
Give AI agents a way to find, create and update a content platform's structured information.
What we built
Backend AI with Anthropic, OpenAI and Google models, 50+ MCP tools, content APIs and semantic search.
What it made possible
Agents that find information, act on it and power interactive experiences inside compatible AI clients, on a platform used by large enterprises and a unicorn startup.
50+
MCP tools built

Backend AI, API and infrastructure engineering, from architecture through launch.

Read case study

Agent platform engineering

  1. Backend AIAnthropic, OpenAI and Google
  2. 50+ MCP tools builtDiscover, create and update content
  3. APIs and searchStructured content and semantic retrieval
  4. Platform infrastructureSupports agents and MCP Apps
The platform's agent capabilities, layer by layer.

How we engage

Forward-deployed engineers, on terms you can plan around.

Build with a forward-deployed team, keep it current with Harness Watch, or start with a Harness Review before you expand what the agent can do.

Forward-deployed build

Initial three-month engagement

Forward-deployed engineers embed with your team and build the agent platform, AI product or the systems behind it, measured with the Harness Method from the first week. Scope, milestones and acceptance criteria are agreed before work starts.

You get: Working software, a scorecard on quality, speed, cost, control and drift, the harness your engineers keep using, release gates, runbooks, and a handoff your team runs with.

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Forward-deployed operations

Monthly retainer, with Harness Watch

After launch, we stay on to run Harness Watch: a verdict on each relevant model release, a monthly scorecard, incident response and ongoing improvements.

You get: Release verdicts, a monthly scorecard, a quarterly autonomy review, incident response, improvements and a leadership readout.

How Harness Watch works

Harness Review

Two to three weeks · fixed price

An AI agent audit across every layer: where the time and money go, a scorecard on five measures and a prioritized fix plan.

You get: Harness Map, Scorecard, model and settings comparison, control findings, fix plan, and the test runner in your repository.

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What are you building—or deciding?

Tell us what you’re building, what is getting in the way, and the outcome you need.