DISSECT
by
Decompose. Test. Trust.
Decompose
Break any AI pipeline into individually inspectable steps.
Test
Replay, compare, and benchmark against ground truth.
Trust
Every AI interaction captured, logged, and auditable.
Built for federal AI governance
NIST AI RMF
NIST AI 600-1
OMB M-24-10
OMB M-24-18
OMB M-25-21
EO 14110
Built by
Our Approach
How the B&A AI Center of Excellence makes AI pipelines auditable
The hardest part of AI governance isn't implementing or wiring up the AI pipeline to do what you want it to. It's being able to see inside the pipeline, prove that you're getting good data, and that your tools are compliant with the regulations inside the agency you work in — and with the sensitive data you may be working with. Most AI systems are treated as a single black box: data goes in, a decision comes out, and nobody can explain what happened in between. DISSECT exists because the Bart & Associates AI Center of Excellence solves that problem. We decompose any AI pipeline into its individual steps, wire each one for observability, and give you the tools to test, benchmark, and trust every piece.
The Problem: Good Tools, No Connective Tissue
There's no shortage of AI tools on the market. The problem is that each one addresses a single slice of the pipeline — and none of them talk to each other. When your AI system spans data ingestion, retrieval, classification, generation, and post-processing, you end up stitching together a patchwork of point solutions with no unified view of what's actually happening end to end.
LLM Observability
LangSmith, Helicone, Braintrust
Logs prompts and responses for LLM calls
Only sees the LLM step. Blind to data prep, retrieval logic, post-processing, and how steps depend on each other.
Evaluation Frameworks
RAGAS, DeepEval, Promptfoo
Scores LLM output quality with automated metrics
Evaluates outputs in isolation. Can't trace a bad score back to a retrieval failure or a preprocessing bug upstream.
ML Experiment Tracking
MLflow, Weights & Biases, Neptune
Tracks model training runs, hyperparameters, and metrics
Built for model training, not multi-step inference pipelines. Doesn't capture prompt-level interactions or step-by-step data flow.
Orchestration
LangChain, LlamaIndex, Haystack
Chains LLM calls, retrievers, and tools into workflows
Helps you build the pipeline but doesn't help you prove it works. No built-in benchmarking, governance reporting, or compliance alignment.
AI Governance Platforms
Credo AI, Holistic AI, IBM OpenPages
Risk assessment, bias detection, policy management
Operates at the policy layer. Doesn't look inside the pipeline to see what the AI actually did on each request.
Vector DB & RAG Tools
Pinecone, Weaviate, ChromaDB
Stores and retrieves embeddings for context injection
Handles one step of the pipeline. No visibility into how retrieved context affects downstream decisions.
Each of these tools is good at what it does. But none of them can decompose your entire pipeline, instrument every step, benchmark end-to-end accuracy, and generate the compliance documentation your agency needs — all in one place. That's the gap.
Where the B&A AI Center of Excellence Comes In
AI pipelines are wildly complex and variable. No two look the same. A document classification system has different steps than a contract analysis pipeline, which has different steps than a chatbot with RAG. The architecture changes, the models change, the data changes — and every combination creates a unique governance challenge.
That's exactly why this can't be solved by a tool alone. It takes a team that understands AI architectures, federal compliance requirements, and the practical reality of how these systems get built and deployed. Our experts sit down with your pipeline — however complex, however custom — and decompose it into a testable, auditable framework that everyone in your organization can understand and trust.
One framework that speaks to every stakeholder
Security Teams
Project Managers
Engineering
Data Scientists
Data Analysts
Executives & Stakeholders
Compliance Officers
Inspectors General
Security needs to know the AI is safe. PMs need to know it's on track. Engineers need to debug it. Data scientists need to improve it. Stakeholders need to trust it. Compliance needs to prove it. DISSECT gives every one of them the same source of truth — because the decomposition makes the pipeline legible to all of them.
How the Process Works
1
Decompose the Pipeline
Our team takes your AI workflow — whether it's a single LLM call or a multi-stage system with retrieval, classification, and generation — and breaks it into individually observable steps. Each step gets a clear definition: what it does, what it depends on, and what it produces. This is the hard part, and it's where our expertise matters most.
2
Instrument Every Interaction
Once decomposed, every AI interaction is captured automatically. Every prompt sent, every response received, every model parameter used. Nothing is hidden or summarized. You get the raw truth of what your AI system is actually doing.
3
Test and Benchmark
With individual steps exposed, you can replay any step with different models, different prompts, or different parameters. Run A/B comparisons. Score against ground truth. Compare providers side by side with real metrics — not marketing claims. Every experiment is logged.
4
Prove Compliance
The audit trail writes itself. Because every interaction, experiment, and decision is already captured, generating governance documentation aligned to NIST AI RMF, OMB mandates, and EO 14110 is a natural output — not an afterthought.
What the B&A AI Center of Excellence Provides
Pipeline Decomposition
We take any AI system — yours or ours — and break it into testable, auditable components. This is the core service. It requires deep understanding of AI architectures, prompt engineering, and federal compliance requirements.
Benchmarking & Validation
We build ground truth datasets, design benchmark suites, and run multi-model evaluations so you know exactly how your AI performs — with numbers, not opinions.
Governance Documentation
We generate the compliance artifacts your agency needs: NIST-aligned reports, audit trails, and risk assessments — all backed by real data from the pipeline itself.
Continuous Improvement
Decomposition isn't a one-time event. As your AI system evolves, we help you track performance over time, catch regressions early, and prove that changes made things better.
For Your AI Systems or Ours
Your AI Pipelines
Already have AI systems in production? We'll decompose them, instrument them for observability, and give you the tools to benchmark and govern them — without changing how they work.
Our AI Services
Need AI capabilities built right? We design and build AI pipelines with DISSECT baked in from day one. Every step is transparent, testable, and audit-ready before it ever reaches production.
AI Center of Excellence
Pipelines
Runs
Run Details
Running…
Step Lab
Isolate, replay, and compare individual pipeline steps
Select Step
Results
Select a run and step to get started
Evaluation Reports
AI-generated governance reports with experiment history and compliance documentation
Generate Report
Select a pipeline and generate a report to see results
Benchmarks
Ground truth scoring — measure pipeline accuracy against verified labels
Ground Truth
Loading…
Run Benchmark
🤖 Models to Benchmark
📐 Pattern Matching Tuning
Without shuffle, the same contracts are selected every time for consistent comparison. Enable shuffle to test on a fresh random sample.
Paste a custom prompt here, or leave empty for the default. Placeholders: {contract_details}, {programs_list}
Running benchmark…
Classifying ground truth contracts
Results
Benchmark History
No benchmarks run yet
AI Governance
How DISSECT answers the federal AI transparency mandate
Federal agencies deploying AI face a consistent set of questions from oversight bodies, inspectors general, and the public: What did the AI do? Why did it make that decision? Can you prove it? Can you improve it? DISSECT was built from the ground up to answer every one of these questions — not with documentation after the fact, but with real-time observability baked into the pipeline itself.
Regulatory Alignment
Every feature in DISSECT maps to specific requirements in current federal AI governance frameworks.
| Framework | Requirement | How DISSECT Addresses It |
|---|---|---|
| NIST AI RMF | GOVERN — Establish accountability structures for AI systems | Every pipeline run, experiment, and parameter change is logged with full provenance. Experiment tracking creates an auditable chain of decisions. |
| NIST AI RMF | MAP — Identify and document AI system context and capabilities | Pipeline decomposition breaks complex AI workflows into individually documented steps. Each step's purpose, inputs, outputs, and dependencies are visible. |
| NIST AI RMF | MEASURE — Quantify AI system performance and limitations | Multi-model benchmarking with scientific metrics (weighted F1, Cohen's κ, confidence calibration). Ground truth scoring with per-class precision/recall. |
| NIST AI RMF | MANAGE — Continuously monitor and improve AI systems | Step Lab enables iterative prompt tuning with A/B comparison. Benchmark history tracks performance over time. Experiment logs capture every change. |
| NIST AI 600-1 | Generative AI transparency — document prompts, outputs, and model behavior | Every LLM call is captured: the exact prompt sent, the exact response received, the model used, temperature, and token parameters. Nothing is hidden. |
| OMB M-24-10 | AI use case inventory and risk assessment | Pipeline registry with step-level documentation. Each pipeline's AI components are individually cataloged with their risk characteristics. |
| OMB M-24-18 | AI procurement — vendor transparency and evaluation | Multi-model benchmarking lets agencies objectively compare AI providers on the same task. Results are stored for procurement justification. |
| OMB M-25-21 | Accelerating AI use through governance and public trust | DISSECT removes the governance barrier to AI adoption. Teams can deploy AI faster because every interaction is already auditable. |
| EO 14110 | Safe, secure, and trustworthy AI development | Snapshot-based replay ensures AI behavior is reproducible. Test suites validate pipeline correctness. Benchmark scoring measures real-world accuracy. |
Questions Agencies Ask — And How DISSECT Answers
"How do we know the AI made the right decision?"
Ground truth benchmarking scores every AI classification against verified labels. Per-program precision, recall, and F1 metrics quantify exactly where the model succeeds and fails. Confidence calibration reveals whether the model knows when it's uncertain.
"What happens inside the AI pipeline?"
Pipeline decomposition breaks the workflow into individually observable steps. Each step's input, output, and processing logic is visible. The DAG view shows data flow and dependencies. Step explanations describe each stage in plain language.
"Can we see exactly what the AI was asked and what it said?"
Every LLM interaction is captured with the full prompt text, model parameters, and complete response. The Step Lab shows these side-by-side. Nothing is summarized or abstracted — you see the raw interaction.
"How do we compare different AI models objectively?"
Multi-model benchmarking runs the same contracts through multiple providers simultaneously. A scientific leaderboard ranks models by accuracy, weighted F1, Cohen's Kappa, confidence calibration, latency, and error type. Results are stored for procurement documentation.
"Can we prove the AI improved after we changed it?"
A/B comparison runs the same data through two configurations and produces a contract-level diff. Experiment tracking logs every change with before/after metrics. Benchmark history shows performance trends over time.
"What if the AI gets it wrong — can we trace why?"
Every benchmark result includes the model's reasoning for each classification. Click any contract row to see why the model chose that answer. Error analysis breaks failures into wrong-match vs. no-match categories. Pattern matching reasoning is captured separately.
"How do we satisfy our IG and oversight requirements?"
Governance reports are generated with NIST AI RMF alignment citations. All experiment data, benchmark results, and pipeline configurations are persisted in a database with timestamps. The full audit trail is always available.
"Can non-technical stakeholders understand what the AI does?"
Step explanations describe each pipeline stage in plain language. Friendly parameter controls replace raw JSON. Summary cards translate technical outputs into human-readable insights. No engineering background required.
What DISSECT Captures
Every prompt sent to every LLM
Every response received, with model reasoning
Model name, provider, temperature, token limits
Full context snapshot at every pipeline step
Every experiment: before/after params and results
Benchmark scores with per-contract reasoning
Timestamps, durations, and version history
Data lineage from input to final decision