AI-Readiness & Data Engineering
Your AI didn’t fail.
Your data did.
We fix the data layer so the AI you already bought stops failing — and we give you the independent assurance that it’s production-ready.
Fixed-price · two weeks · read-only · evidence from your real data
The problem
The AI you bought is stalling — and it’s fixable.
Most stalled AI doesn’t fail on the model. It fails in the layer beneath it — fragmented data, missing integration, and a workflow the tool was never fit to. That layer is fixable, and fixing it is the work we do.
AI bought from specialized external vendors succeeds about twice as often as internally-built systems (~67% vs ~33%).MIT NANDA — State of AI in Business, 2025
- 95%
of enterprise generative-AI pilots deliver no measurable P&L impact.
MIT NANDA — State of AI in Business, 2025 - >40%
of agentic-AI projects will be cancelled by the end of 2027 — unclear value, weak controls.
Gartner, 2025 - 12%
of organizations have data of sufficient quality and accessibility for AI; data quality is the #1 obstacle (43%).
Informatica — CDO Insights, 2025
Positioning
We’re not an AI consultancy. We’re the engineers who fix the data layer so the AI you already bought stops failing — and the independent assurance that it’s production-ready.
Assurance, not labor
The durable value isn’t commodity cleanup. It’s the diagnosis, the re-measurable score, the attestation, and the cross-system judgment that this will hold in production.
Private AI when it’s confidential
For data that can’t leave your walls, we fix it andrun private AI on it — models that stay inside your own infrastructure.
The instrument
Seven dimensions, scored on your actual data
Not a questionnaire. A read-only harness profiles the real systems and scores each dimension 1–5. The seven roll up into the composite readiness score.
- D1
Data Inventory & Access
Is the data identified, located, and actually reachable?
- D2
Data Quality
Completeness, accuracy, consistency, duplication, freshness — the #1 killer.
- D3
Integration & Unification
One queryable layer, or siloed systems with no seam between them?
- D4
Use-Case ↔ Data Fit
Does the data the use case needs exist, usable, at production volume?
- D5
Pipeline & Production-Readiness
An automated, repeatable pipeline — or hand-assembled before each run?
- D6
Governance, Security & Compliance
Permissions, PII, residency — also where private AI opens up.
- D7
Success Metrics & Observability
Defined KPIs and any way to tell whether the AI is actually working?
The engagement
Two weeks, fixed scope, read-only
A productized 2-week diagnostic. Read-only throughout — nothing in your systems changes until you decide it does.
- 01
Kickoff & read-only access
Intake, the target AI use case, and secure read-only access to the systems in scope.
- 02
Discovery
The harness profiles your real data — evidence from the actual estate, not a survey.
- 03
Synthesis
Root causes, the 0–100 readiness score, and a prioritized remediation roadmap.
- 04
Delivery
The report, a 60-minute readout, and a fixed proposal for the fix.
Sample report · illustrative data
What the audit hands you
The 0–100 score, the seven scored dimensions, the root causes behind them, and the roadmap to fix them — in one readout.
Root-cause findings
- Data Inventory & Access. Sources identified, but two live behind manual exports.
- Data Quality. 38% of the records the agent reads are missing a required field.
- Integration & Unification. Four systems, no pipeline between them.
- Use-Case ↔ Data Fit. The pilot ran on hand-curated data that doesn't exist in production.
- Pipeline & Production-Readiness. Data is assembled by hand before each run.
- Governance, Security & Compliance. Permissions solid; PII handling needs a residency review.
- Success Metrics & Observability. No defined KPI; no way to tell if the AI is working.
Prioritized roadmap
- P1Data Quality — scored 2/5. 38% of the records the agent reads are missing a required field.
- P2Integration & Unification — scored 2/5. Four systems, no pipeline between them.
- P3Pipeline & Production-Readiness — scored 2/5. Data is assembled by hand before each run.
Illustrative sample, not a real engagement. A redacted real readout replaces this after the first client.
Offer
The audit is the wedge. The retainer is the destination.
A published price qualifies the buyer. Start with a fixed-price diagnosis; the relationship lands on maintained readiness.
- 01
AI-Readiness Audit
$5,100 standard · $8,600 complex
The wedge: a fixed-price, two-week diagnosis of why your AI is failing.
50% credited toward remediation within 30 days.
- 02
Remediation Build
$9,100–$34,000
The fix: integrate the systems, build the pipelines, clean the data — AI-accelerated.
Scoped to your estate.
- 03Where it lands
AI-Readiness Monitoring + Managed Pipelines
from $1,700/mo
The destination: a re-measured score, operated pipelines, maintained readiness.
Recurring. Where the relationship lands.
PremiumFor confidential estates, remediation and the retainer can run on private AI that never leaves your infrastructure— the same readiness, inside your own walls.
For implementation partners
Built the AI, stalled on the data? Hand us the plumbing.
You sold and built the AI. When it stalls on the client’s data, hand off the data-readiness and remediation — white-labeled or co-branded, at a wholesale rate. You keep the relationship and the credit; the client’s tool finally works.
- White-labeled or co-branded. The work ships under your name, or ours alongside yours.
- You keep the client.No competition for the relationship — we do the plumbing and step back.
- Wholesale rate. Priced for resale, so it works inside your margin.
About
The engineers you actually talk to
Darkbloom Digital is a senior data-engineering practice — not an account manager routing you to a delivery team. You work directly with the engineers who read your data, write the findings, and build the fix.
Our focus is narrow on purpose: cross-system integration and data quality, the layer where bought AI actually stalls. Remote-first, working across US and European time zones, with transparent USD pricing. Built lean by conviction — right-sized models and clean pipelines, because that’s cheaper, faster, and easier to maintain.
- Python & data engineering
- dbt & pipeline orchestration
- Cross-system integration
- Data-quality profiling
- Private / self-hosted LLMs
- Governance & residency
Contact
Two doors. Pick yours.
Book an AI-Readiness Audit
A fixed-price, two-week diagnosis of why your AI is stalling — and the roadmap to fix it. For teams running the tool.
Book a callOr email admin@darkbloom.digital.
Partner & white-label
An implementation partner who hit the data wall on a client’s AI? Hand off the plumbing at a wholesale rate and keep the relationship.
Partner enquiryEmail dev@darkbloom.digital.