For platform teams
You know what a harness is.You also know yours could be better.
Embedded engagements for AI platform teams who want senior harness engineering without the cost and politics of hiring it in-house.
Speaking your language
This page assumes you've already done the work.
You're an AI platform team at a Series B+ company. You have ten or twenty AI features across the product. You have a tracing setup (Langfuse, LangSmith, or something homegrown). You have at least one eval framework limping along. You probably have someone on the team who's read the GEPA paper and someone who's evaluated DSPy.
You also know that the harness layer across those ten or twenty features is uneven. Some are well-structured. Some were inherited from product teams who shipped fast and moved on. Some were last touched eight months ago and quietly degraded when you swapped models. You don't have the bandwidth to do an honest audit across all of them, and the work falls into the gap between “platform infrastructure” and “feature team responsibility”, owned by nobody.
We do that work.
How we engage with platform teams
Engagements that fit how you actually work.
Four weeks
Cross-feature audit
from $60,000A diagnostic across five to ten of your AI features simultaneously. We map the harness for each, identify common failure modes across the portfolio, and deliver a prioritized remediation plan with rough effort estimates. Useful when you're trying to figure out where to spend the next quarter of platform investment.
Start here8–12 weeks
Most requestedEmbedded harness engineering
from $120,000A senior engineer from our team works alongside your platform team doing the harness rebuilds your team has been queueing up. They write code that goes into your codebase, attend your standups, and pair with your engineers on the hard parts. They don't replace anyone. They accelerate what your team is already trying to do.
Start hereFour weeks
Methodology transfer
from $40,000A four-week engagement designed to leave your team capable of doing this work themselves. We rebuild one harness end-to-end with your team observing, document the methodology in your internal tooling, run a workshop on the failure-mode taxonomy, and hand off the eval-set construction process.
Start hereAll three are available as one-time engagements or as recurring annual programs. All include the standard money-back guarantee on remediation.
What we don't do
We don't replace your platform team.
The point is to accelerate your team, not to make them dependent on us. Every engagement leaves artifacts your team owns and methodology your team can extend.
We don't sell tooling.
We don't have a platform you log into. We don't have a SaaS dashboard. We deliver harnesses, eval sets, and methodology: concrete artifacts that go into your codebase. If you want a SaaS, there are good ones and we'll point you to them.
We don't compete with your existing observability stack.
Whatever you have for tracing (Langfuse, LangSmith, Datadog, Arize, homegrown) we work with it. We instrument inside your existing setup, not on top of a new one.
The methodology, in your language
We work on the harness layer as defined in the Meta-Harness paper.
We work primarily on the harness layer as defined in the Meta-Harness paper (Lee et al., 2026): the executable scaffolding around a frozen LLM, including retrieval, prompt assembly, conversation memory, tool use, output validation, and retry/fallback logic.
Our diagnostic methodology draws on three threads: the Meta-Harness paper's empirical finding that raw context access dominates summarized context (their Table 3 ablation), GEPA-style reflective prompt optimization (Agrawal et al., 2025/2026) for the prompt-level rebuilds, and the broader DSPy lineage for systematic harness construction.
We're opinionated about what doesn't work. We don't believe that swapping in a higher-end model is a substitute for harness engineering. We don't believe in pre-built prompt libraries. We don't believe in “AI agent platforms” as a category. The platforms abstract away the layer where the work actually has to happen.
If any of this is wrong by your lights, the call would be a good one.
Who this is for
You lead, or are senior on, an AI platform team at a Series B+ company. You have multiple shipped AI features. You've already invested in observability and eval tooling. You know the harness layer needs work and don't have the bandwidth to do it well in-house. You'd rather buy senior engineering on a project basis than try to hire it.
Who this isn't for
You're pre-platform. You're still figuring out which observability tool to buy. You want a SaaS product, not an engagement. You want us to recommend models or rewrite your application. That's not the layer we work on.
The first conversation is twenty minutes,no obligation.
We'll talk through your portfolio and tell you which engagement shape fits. If we don't think we can help, we'll say so directly.