MOD.01 / Engineering
AI Agent Development
Custom agents shaped to your business — not a template. Support, research, analysis, internal tools. We own the loop: prompts, tools, memory, evals.
IN PRODUCTION
50+ agents shipped
evals green in CI
cost/resolution measured
SINCE 2025
Replyant // AI Systems Engineering
Build agents that survive the next release cycle.
We design, ship, and operate AI agents and automation that pay their keep — measured end to end, with numbers your CFO can defend. No demoware.
We keep the work focused so your operators can trust what we build — and so we can defend every line of it.
MOD.02 / Operations
Business Automation
We find the repetition, map the handoffs, and ship pipelines that quietly save hours — integrations across your stack without breaking what works.
MOD.03 / Strategy
Strategic Consulting
Where does AI earn its keep, and where does it just burn cash? A clear roadmap, with numbers, that survives the next hype cycle.
From first hypothesis to fifty thousand production calls. Each phase has its own deliverable, its own exit criteria, and its own honest go/no-go. We will tell you to stop before we tell you to scale.
Phase 01
Discover
We map the workflow, the data, and the politics. One week, sometimes two.
You leave with a shortlist of agent-shaped problems and a refusal to chase the ones that fail the sniff test.
Phase 02
Design
We pick the smallest agent that proves the thesis and sketch its evals first.
Tool surface, memory, escalation, failure modes — decided on paper before a token is spent.
Phase 03
Ship
Six to twelve weeks. Real users, real data, evals running in CI from day one.
Behind a feature flag, in shadow mode, or to a single team. Production is a verb here.
Phase 04
Operate
Agents that survive the next release cycle, not just the demo.
We keep the eval suite green and the cost curves honest — or hand your team the keys and the docs.
Allergic to demoware. If the agent in your roadmap has to clear legal, please finance, and survive a Tuesday outage — we are the right call. If you need a hype video, we are the wrong one.
SRC.01 / Operations
Ops Leaders
You run the function the agent will touch — support, finance, RevOps, supply chain. The headcount math is unforgiving and a stalled pilot is now political.
We close the gap between "the model can do it" and "the team trusts it at 5pm on a Friday."
SRC.02 / Leadership
Founders & CEOs
You have an AI line item on the board deck and a quarter to make it real. The roadmap depends on agents you have not built yet.
We turn the slide into a system before the next board meeting compounds the debt.
SRC.03 / Platform
CIOs & CTOs
You own the platform under the agents — identity, data, observability, risk. The business keeps approving pilots; you keep absorbing the sprawl.
We install the governance that lets you say yes without inheriting the chaos.
Agentic AI in Finance: The Operations Playbook for 2026
How finance teams deploy AI agents in production — month-end close, AP/AR, reconciliation, FP&A — and the governance model that makes it work.
Build vs. Buy AI Agents: A 2026 Decision Framework
Forrester says ~75% of self-built agent projects fail. A neutral build vs. buy vs. hybrid framework, plus the TCO crossover that flips the math.
Agent Washing: A Buyer's Field Guide to Fake AI Agents
Vendors call everything 'agentic.' Gartner says ~130 of thousands actually are. A buyer's rubric to spot agent washing before you sign.
Agentic RAG: Adaptive Retrieval Patterns That Scale
How agentic RAG beats naive pipelines: agent-controlled retrieval, query routing, verify-then-retrieve loops, and guardrails that prevent infinite loops.
MCP Tool Poisoning: A Working Exploit and How to Stop It
Tool poisoning hides malicious instructions in MCP tool descriptions models trust but users never see. A working exploit, the rug pull, and defenses in code.
MCP Goes Stateless: Refactoring for the 2026-07-28 Spec
The 2026-07-28 MCP RC drops the initialize handshake and Mcp-Session-Id, moves client info to _meta, and adds routing headers. What to refactor.
Five rules we hold even when the engagement is on fire — how we keep agents in production after the launch post stops trending, and defensible when the auditor walks in.
- 01
Anti-hype.
We will say no to the agent that does not pay for itself. The deck does not move the metric; the system does. Every recommendation is one we would defend in your QBR.
- 02
Evals before ship.
If we cannot measure the behavior, we will not deploy the behavior. Eval suites are written before the prompt is, and they run in CI for the life of the agent — not just the launch.
- 03
Agents that survive the next release cycle.
Model providers ship breaking changes; vendors get acquired; APIs deprecate. We build for the second year — versioned prompts, pinned tools, and a tested rollback path.
- 04
Numbers your CFO can defend.
Every engagement comes with a measurement layer your finance team can sign. We track unit economics — cost per resolution, per draft, per decision — not vanity tokens-per-second.
- 05
Own the loop end-to-end.
Prompts, tools, memory, evals, observability, and on-call runbook — one team, one accountable owner. We hand you an operating manual, not a half-built system and a Slack channel.
Free resource // Spec sheet
The AI Readiness Checklist.
A practical, opinionated guide to evaluate where AI fits in your business — and what to do first. Thirty-two checkpoints, zero fluff.
- Score a workflow before you automate it
- Spot the failure modes that kill pilots
- Build the measurement layer your CFO will sign
Request // checklist.pdf
No spam. One email, the checklist, done.