11AugEngineering
Why most RAG systems fail in production
Retrieval-augmented generation demos are easy and production RAG is not. The failures are predictable, and almost all of them happen in retrieval rather than generation.
8 min readAI · Cyber · Digital
NivaMind combines artificial intelligence, cybersecurity, data, engineering and transformation to turn technology into measurable business advantage.
Why we exist
AI alone is not transformation. Neither is cloud, security or engineering. What changes a business is all four sequenced against one outcome, with someone accountable for the number at the end.
Why teams call us
The uncomfortable answers, early
We will tell you not to build it
A meaningful share of discovery engagements end with a recommendation against the project. That is the engagement working, not failing.
Measured, not asserted
Every system we ship carries an evaluation harness. If we cannot measure whether it improved, we do not claim that it did.
Your engineers own it
Code lands in your repositories, in your stack, with your team pairing on it. Lock-in is a business model, not an architecture.
Cost is a design constraint
Inference spend is designed in from the first week, not discovered in the first invoice after launch.
What we do
Five disciplines. One operating model.
Engagements are scoped to one of these, or sequenced across several when the outcome needs more than one.
S / 01
Artificial Intelligence
Enterprise AI, generative AI, agents and automation.
S / 02
Cybersecurity
Secure by design. Resilient by architecture.
S / 03
Data and Cloud
Turning enterprise information into intelligence.
S / 04
Digital Engineering
Applications, platforms, DevSecOps and modernization.
S / 05
Transformation
Strategy translated into measurable execution.
The 6D Loop
From a question to a system that keeps earning its place
This is the loop that runs inside the pilot and production stages, not a second model alongside them. The return path from operation to build is the part that matters: an AI system nobody revises is one nobody is measuring.
- 01DefineBusiness question and success measures
- 02DiscoverData sources, quality, ownership, access and contracts
- 03DevelopRetrieval, models, orchestration and logic
- 04DecideGolden datasets, evaluations and quality gates
- 05DeployGuardrails, release and rollout
- 06DriveOperations, cost, drift, quality and continuous improvement
Evaluation runs continuously once a system is live, and what it finds returns to the build stage.
Technical ground
We work in your stack, not around it
Everything we build lands in your cloud, your repositories and your deployment pipeline. This is the ground we are already fluent in.
Where it runs
Models
Data platform
Retrieval
Runtime
Security and identity
Observability
Know about us
Convergence is the product, not the pitch.
One team, start to finish
The people who write your roadmap are the people who write your code, which makes the roadmap considerably more honest.
Selected work
Placeholder: document-heavy process, turned around
One sentence naming the before and after. Replace with what this engagement actually changed for the organization.
Client outcomes
Example content. Replace with approved client quotes before publishing.
PLACEHOLDER. The strongest opening quote names a number. Something like: the team cut our review backlog from three weeks to two days, and we did not add headcount to do it.
Client nameTheir role, their organization
How we work
Four stages, and permission to stop after any of them
Each stage ends with a decision point and something you own outright. If the answer at the end of discovery is that the case is not there, you have spent a few weeks instead of a year.
- 011 to 3 weeks
Discovery
Find the cases worth building and the ones worth killing.
- 024 to 8 weeks
Pilot
Build the narrowest useful version and measure it honestly.
- 038 to 16 weeks
Production
Harden, integrate and put it in front of everyone who needs it.
- 04Ongoing
Enablement
Hand it over properly, so you do not need us to keep it running.
Insights
What we have learned building this
- 11Aug
28JulEvaluationEvaluating LLM features without fooling yourself
A practical evaluation stack: golden sets, LLM-as-judge with its known biases, regression gates in CI, and the metrics that actually correlate with user trust.
10 min read
14JulEconomicsThe real cost of an AI feature
Token pricing is the smallest line in the budget. Here is the cost model we use with clients, covering inference, retrieval infrastructure, evaluation, human review and the ongoing maintenance nobody scopes.
7 min read
Let's build together
Let's find out if AI is worth it for you
A short conversation is usually enough to tell whether there is a real case here, and we will say so if there is not.