When your AI initiative is shadowed by hallucinations, the cause is rarely the model.
Most enterprise RAG and AI systems that underperform are failing for reasons their original builders cannot easily admit — incomplete document curation, weak retrieval discipline, and the absence of human-verified quality. We diagnose what is actually wrong, and we remediate it.
The Pattern We See Across the Enterprise AI Market
If your AI project followed any of these stages, you are not alone — and the next step is rarely "another model upgrade."
An AI initiative is launched under board or executive pressure with strong early commitments.
A roadmap recommends a standard RAG deployment using commodity components.
The system is built and goes live with acceptable behavior in curated demos.
Real users surface quality and accuracy problems within three to six months.
The team labels the issues "hallucinations" and recommends model upgrades or platform changes.
The upgrades do not resolve the problem. Confidence in the investment erodes.
Here is what is usually true: the model is not the problem. The problem is upstream — in the documents being retrieved, in how they are chunked, in what is missing from the corpus, and in the absence of a human-verified evaluation discipline. None of those issues are visible in vendor demos. All of them surface in production.
Our Approach
We are specialists in the retrieval and knowledge layer of enterprise AI — the part that determines whether your system is trustworthy, regardless of which model sits on top.
Honest Diagnosis
A 3-5 week assessment of your existing RAG or AI implementation. We sample your real documents, run real queries, grade real outputs, and produce a written report with specific failure examples and root-cause analysis.
- Concrete failure library from your own system
- Categorized by data, retrieval, and generation issues
- Prioritized remediation roadmap
- Executive and technical readouts
Curation Services
Pure human document preparation, chunking, metadata tagging, and ongoing corpus refresh. Files in, files out. We never touch your systems — your platform, your AI, your infrastructure stay yours.
- Document cleanup and deduplication
- Human-verified chunking with context preservation
- Metadata schema and tagging
- Ground truth evaluation sets
- Continuous corpus refresh
Bespoke RAG Engineering
For problems commodity platforms cannot solve. Custom-built retrieval systems for unusual document structures, sovereignty constraints, safety-critical accuracy requirements, and novel retrieval patterns.
- Sovereign and air-gapped deployments
- Complex regulated documents
- Multi-hop and temporal reasoning
- Permission-aware retrieval
- Safety-critical accuracy domains
How a Diagnostic Engagement Works
Most relationships begin with a fixed-fee diagnostic. It is the lowest-risk way to find out whether the problem you have is the problem you think you have.
Intake and Scope
We meet your team, understand the use case, define success criteria in writing, and arrange secure document transfer. Nothing is installed in your environment.
Corpus and Retrieval Sampling
We sample your documents, your chunks (if available), and the retrieved outputs your system produces for real user queries.
Human-Graded Evaluation
Our trained reviewers grade outputs against the source documents. We build a failure library — actual examples of where your system goes wrong, and why.
Root-Cause Analysis
We categorize failures into data, chunking, retrieval, generation, and process layers. We identify which fixes will move the most quality, and which will not.
Findings and Roadmap
You receive a written assessment, a failure library, and a prioritized remediation roadmap. Separate readouts for your executive sponsor and your engineering team.
After the Diagnostic
You decide what happens next. You can take our roadmap and execute internally, engage us for curation services, engage us for bespoke remediation, or stop. The diagnostic is complete and yours regardless.
Our Quality Commitment
The market has trained buyers to be skeptical. Here is exactly how we are different — in writing, before we start.
Success Criteria in Writing
Every engagement starts with measurable, written success criteria in the Statement of Work — before work begins. No moving goalposts.
Baseline Measured First
We measure your existing system's quality on a real evaluation set before we change anything. The "before" picture is signed and dated.
Humans Grade Quality, Not AI
All quality grading is performed by trained human reviewers, not by AI judges. We track inter-rater reliability and disclose it.
Independent Final Audit
Final measurement is performed by a reviewer who did not build the system, observed by your team. You see the raw results.
Reproducible Package
You keep the complete evaluation package — questions, ground truth, system answers, human grades. You can re-run it any time.
You Own What We Build
Methodology, evaluation sets, decision logs, and architecture documentation transfer to you. No lock-in. No black boxes.
Where We Focus
Vertical depth matters. We focus on three industries where AI quality failures are most consequential and where our team has the deepest experience.
Government & Regulatory
Federal AI pilots facing accuracy, FedRAMP, and accreditation pressure. Sovereignty-aware deployments. Policy, regulatory filings, technical manuals, and inspection workflows.
Available through prime subcontracting and direct contract vehicles.
Healthcare
Clinical documentation, regulatory submissions, medical literature, and patient-facing communication. HIPAA-compliant handling. Reviewers with clinical documentation backgrounds.
BAA-ready. PHI handled under documented protocols.
Banking & Financial Services
Internal policy search, regulatory compliance, customer service augmentation, and research workflows. Auditability and traceability built into every deliverable.
Examiner-ready documentation.
What We Don't Sell
Specialization is a discipline of saying no. The practice deliberately does not deliver the following — and politely refers prospects elsewhere when these are what they need.
- Templated or commodity RAG builds that fit standard cloud platform patterns
- AI strategy consulting, roadmaps, or transformation advisory as a standalone offering
- Managed SaaS or hosted AI platforms
- Fine-tuning or model training services
- Generic prompt engineering or chatbot development as standalone work
- Large-scale data labeling — offshore BPO firms deliver this more efficiently
We focus on the specific, demanding, often-unfashionable work that determines whether enterprise AI actually performs in production. That focus is the entire offering.
Talk to an AI Quality Expert
Whether your RAG is in production and underperforming, or you are planning a new initiative and want to avoid the common failure modes — let's have a 30-minute conversation.