Built on DSPy
How we keep your workflows reliable
Every DecarbDesk workflow runs on a reliability layer that evaluates outputs, catches regressions, and enforces quality thresholds before anything reaches your team or your customers.
The problem with AI in production
AI workflows look reliable in demos. Then a model provider updates their weights, a new document format arrives, or seasonal patterns shift the data distribution. The workflow that handled 95% of cases correctly last month now handles 88%. Nobody notices until a customer complains or a report has wrong numbers.
The standard response is manual spot-checking: someone reviews a sample of outputs every week and hopes the sample is representative. That does not scale, and it catches problems after they have already caused damage.
We replace spot-checking with a structured evaluation pipeline built on DSPy, Stanford's open-source framework for programming and optimizing language model pipelines. Every workflow output gets scored by calibrated evaluators. Quality trends are tracked over time. Regressions trigger alerts before they reach production. The question shifts from "is this good?" to "does this meet the threshold we set, and can we prove it?"
Two layers, one system
Every workflow separates the orchestration shell (deterministic, policy-enforcing) from the AI layer (reasoning, non-deterministic). The reliability layer sits between them.
Orchestration Shell
Deterministic code that enforces structure: triggers, routing, approval gates, hard caps, audit logging, and kill switches. This layer never guesses. It follows rules.
Reliability Layer
Built on DSPy. Evaluates every AI output against calibrated metrics. Tracks quality scores over time. Blocks deployments that miss thresholds. Optimizes prompts and configurations automatically.
AI Layer
Language models that read documents, draft emails, classify tickets, and make judgment calls. Non-deterministic by nature. The reliability layer constrains this so it stays within bounds.
Why DSPy
Most AI automation relies on hand-written prompts. When output quality drifts, someone rewrites the prompt, tests it manually, and hopes it holds. That is fragile.
DSPy treats the entire language model pipeline as a program that can be compiled, evaluated, and optimized automatically. Instead of tweaking prompts by hand, we define the objective (what a good output looks like), provide examples, and let DSPy's optimization loops find the best configuration. When model providers update their weights or your data distribution shifts, we re-optimize against the same objective. Quality is maintained programmatically, not manually.
This means your workflows improve over time without anyone hand-tuning prompts. The evaluation framework measures output quality against versioned datasets. The optimization framework finds better configurations. The deployment gates ensure that no update ships if quality scores drop below your threshold. The whole loop runs automatically.
Three guarantees
Evaluations you can measure
Every workflow output gets scored against defined metrics. We calibrate those metrics against known-good examples so you know when the score is trustworthy and when it is not. Evaluator accuracy is a tracked metric, not an assumption.
Results you can reproduce
Datasets and evaluation suites are versioned. When you compare this week's results to last week's, you are comparing against the same inputs, the same metrics, and the same thresholds. No ambiguity about what changed.
Gates you can enforce
Quality gates run before every deployment. If scores drop below the threshold you set, the update blocks. Regressions do not ship. Your team can audit every decision with structured artifacts.
What this means for your team
If you run operations
Fewer incidents. When a workflow output drifts, the evaluation layer catches it before a customer sees the result. The monthly ops report shows quality scores alongside the usual throughput metrics.
If you run finance
Lower AI spend. The optimization layer finds configurations that produce the same quality at lower token cost. Multi-provider routing means you pay the best rate for each model call, not just the default.
If you care about compliance
Audit trails that hold up. Every workflow run produces a structured record: what inputs went in, what model was called, what outputs came out, what score the evaluator assigned. Exportable, searchable, and version-controlled.
How this connects to your managed workflows
Every DecarbDesk workflow runs through the DSPy evaluation pipeline. When the collections workflow drafts a follow-up email, the reliability layer scores the draft before it reaches the approval gate. When the support triage workflow categorizes a ticket, it verifies the classification against known examples. When the reconciliation workflow matches a bank transaction, it checks the confidence score against the threshold you set.
You never interact with the evaluation layer directly. Your team sees the workflow outputs in Slack, Sheets, and Gmail. The reliability infrastructure runs behind the scenes and surfaces only when something fails a check, in which case the workflow pauses and alerts your team instead of proceeding.
The monthly ops efficiency report includes reliability metrics: average quality scores per workflow, score trends over time, number of outputs that failed quality gates, and evaluator accuracy. This is how "gets better over time" becomes measurable instead of aspirational.
DSPy turns AI quality into a contract: measured, reproducible, and enforced in production.
Sample Ops Efficiency Report
Every managed client gets a monthly report with metrics like these.
12 hrs/week
saved
3x faster
invoice collection
68%
fewer manual errors
2-day
cash forecast accuracy
See it in action
Book a 15-minute fit call. We will show you how the reliability layer works inside the specific workflow you care about.