Every action authorized
Per-caller, per-resource checks on every tool call, with human approval wherever money or customer records move.
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.
Retries that never double-post
Idempotent tool actions, so a timeout or a repeated request cannot execute the same operation twice.
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 a unicorn startup and Fortune 500 companies: 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, for a unicorn startup and Fortune 500 companies.
- 50+
- MCP tools built
Backend AI, API and infrastructure engineering, from architecture through launch.
Read case studyAgent platform engineering
- Backend AIAnthropic, OpenAI and Google
- 50+ MCP tools builtDiscover, create and update content
- APIs and searchStructured content and semantic retrieval
- Platform infrastructureSupports agents and MCP Apps
How we engage
A forward-deployed team, on terms you can plan around.
Build with a forward-deployed team, keep it running on a monthly retainer, or start with a review before you expand what the agent can do.
Forward-deployed build
Initial three-month engagement
A forward-deployed team embeds with yours and builds the agent platform, AI product or the systems behind it. Scope, milestones and acceptance criteria are agreed before work starts.
You get: Working software, the harness your engineers keep using with coding agents, evaluation and release gates, runbooks, and a handoff your team runs with.
Explore this optionForward-deployed operations
Monthly retainer
After launch, we stay on to run the evaluations, keep the harness current as models change, respond to incidents and ship improvements.
You get: Evaluation and cost reporting, harness upkeep, incident response, ongoing improvements and a leadership readout.
How the retainer worksArchitecture & Harness Review
Two to three weeks
An independent read of your AI system or your team's agentic engineering workflow, ending in a prioritized plan.
You get: System or workflow map, risk register, evaluation and release plan, harness gap list, and a decision brief with the next scope.
Explore this optionGo deeper
Read how we approach it.
Start a conversation
What are you building—or deciding?
Tell us where you are. We reply within one business day, and the first conversation ends with a recommended next step.