Technical Approach - TekCapitol

Products — Audit · Consulting · AgentOps

Three ways to work with Kyklos360. The methodology (seven phases) is the same; delivery format differs.

Live today

Kyklos360 Audit

Self-serve readiness assessment at tekcapitol.com/kyklos/. Upload-based, platform-agnostic — Snowflake to Cadence EDA to custom internal DBs. Free sample agent workflow tours (25 scenarios — readiness previews, not live agents). Request access to audit your own workflows.

47 platforms 25 agent demos 7 diagnostic dimensions NIST AI RMF

This page

Consulting implementation

What TekCapitol engineers on your existing stack after assessment — ingestion pipes, dbt models, LangGraph agents, RAG, MCP tool connectivity, production monitoring. Fixed-scope engagements ($25K–$80K build phases).

dbt Airflow LangGraph MCP

Roadmap

Kyklos360 AgentOps

Live connectors, saved audits, runnable agents, enforced kill switches and circuit breakers. Govern and Monitor panels in Audit today are recommendations — AgentOps is when those become operational.

connectors persistence runtime

How the three frameworks relate

Kyklos360 uses seven methodology phases (macro journey), ten Audit app phases (screens you walk through), and seven diagnostic dimensions (scores for each workflow step). They nest — they are not three names for the same list.

7Methodology
phases

Assess → Connect → Validate → Transform → Orchestrate → Govern → Monitor → Trust — the consulting and product methodology. Sample reports score the environment across these phases (0–4 each). Homepage, engagements, and implementation sections on this page all use this frame.

10Audit app
phases

Define → Decompose → Confirm → Ingest → Diagnose → Transform → Orchestrate → Govern → Monitor → Report — the live UI at tekcapitol.com/kyklos/. Each phase maps to one or more methodology steps; Decompose also produces workflow steps (typically 5–9 per agent) that are scored individually in Diagnose.

7Diagnostic
dimensions

Data · Semantic · Entity · Rules · Truth · Governance · Connectivity — applied per workflow step during Diagnose. This is the granular scoring engine inside Audit (and informs consulting depth). Not a seventh methodology step.

Methodology (7)	Audit app phases (10)	Diagnostic dimensions (7)
Assess	Define, Decompose, Confirm, Ingest	Data, Source of truth
Connect	Ingest, Diagnose	Entity resolution, Connectivity
Validate	Diagnose	Business rules, Semantic clarity
Transform	Transform	Semantic clarity
Orchestrate	Orchestrate	Governance (routing, tool use, multi-agent)
Govern	Govern	Governance (access controls, approval gates, audit trails)
Monitor	Monitor, Report	Governance (observability, kill-switch readiness)

Consulting sample reports use the methodology phases at environment level (e.g. 14.5/24). Kyklos360 Audit uses the seven diagnostic dimensions at each decomposed workflow step. Same methodology, different granularity. AgentOps (roadmap) will operationalize Orchestrate, Govern, and Monitor recommendations Audit generates today.

Honest boundary: Audit does not run live connectors, save audits across sessions, or execute agents. It tells you what's blocking production and what to build next. Sections below describe how we build that next layer in consulting engagements.

Open Kyklos360 Audit B2B sample report EDA sample report

Kyklos360 · Connect · Validate · Monitor

Data readiness on your existing stack

Before an AI agent can act reliably, your data has to be connected, clean, validated, and observable across the platforms you already own. We extend what you have; we don't rip it out and start over.

Stage 01

Ingest

What we do

Wire your existing sources—Snowflake, Databricks, Salesforce, SAP, APIs, documents, and cloud storage—into a unified ingestion layer. We work with what you already run, not a greenfield rebuild.

Business problem solved

Your agents can't use data they can't see. Siloed systems mean incomplete context, which means wrong outputs.

Tools we use

Apache NiFi Kafka AWS S3 Fivetran Airbyte REST APIs

Stage 02

Process

What we do

Move and transform data through your warehouse and streaming layers—dbt, Spark on Databricks, Snowflake pipelines, or native ELT—cleaning, deduplicating, and normalizing for agent use.

Business problem solved

Raw data from disparate systems is inconsistent and redundant. Processing creates the clean, unified layer agents need to make reliable decisions.

Tools we use

dbt Apache Kafka Apache Spark Databricks Redis dbt Core

Stage 03

Store

What we do

Route processed data to the right store, structured data to Snowflake or your existing warehouse, unstructured to Elasticsearch, embeddings to a vector database for RAG.

Business problem solved

Agents need different data in different formats. One warehouse can't serve all agent types, routing to the right store determines what agents can and can't answer.

Tools we use

Snowflake Elasticsearch Pinecone Weaviate Milvus

Stage 04

Validate

What we do

Apply business logic rules across every data flow. Check for anomalies, schema drift, freshness violations, and completeness before data enters the agent layer.

Business problem solved

ELT validates that pipelines ran. We validate that data makes business sense. An agent acting on technically-correct but contextually-wrong data causes real damage.

Tools we use

Great Expectations dbt tests Monte Carlo Deequ

Stage 05

Monitor

What we do

Instrument continuous observability across the full pipeline, data quality scores, pipeline health, agent output scoring, drift detection, and alerting.

Business problem solved

Going live is not the finish line. Data drifts, schemas change, business rules evolve. Without monitoring you find out something went wrong when a customer calls.

Tools we use

Kibana Monte Carlo Grafana LangSmith Arize

Kyklos360 · Orchestrate

Data Orchestration: Two Layers,
One Coordinated System

Orchestration is the most misunderstood word in AI infrastructure. It means two distinct things, and both have to work before your agents can operate reliably.

Layer 01

Data Pipeline Orchestration

Coordinating how data moves, transforms, validates, and lands across your entire stack. This is the infrastructure layer, making sure the right data reaches the right place at the right time, reliably and repeatably.

→ Schedule and sequence pipeline jobs across sources, warehouses, and transformation layers

→ Handle dependencies, transformation B only runs after ingestion A succeeds

→ Manage failures gracefully, retry logic, alerting, fallback paths

→ Keep data fresh, SLA-driven refresh cycles matched to agent decision speed

Apache Airflow dbt Apache Kafka Apache Spark Prefect Databricks Workflows

Layer 02

AI Workflow Orchestration

Coordinating how AI agents plan, act, retrieve, hand off, and escalate. This is the agent layer, making sure the right agent handles the right task, with the right data, under the right governance controls.

→ Planner-executor patterns, one agent decomposes the task, others carry it out

→ Tool connectivity via MCP, agents connect to your systems through a standard protocol

→ Human-in-the-loop gates, approval checkpoints before irreversible actions

→ Multi-agent coordination, specialized agents working as a governed team

LangChain LangGraph MCP CrewAI LlamaIndex n8n

Why both layers matter (and why most firms only build one)

Data pipeline orchestration without AI workflow orchestration gives you clean data your agents can't use effectively. AI workflow orchestration without data pipeline orchestration gives you agents acting on stale, dirty, or incomplete data. Kyklos360 builds both layers, and the governance infrastructure that sits across them.

Kyklos360 · Transform

Data Preprocessing for AI

Transforming data for a BI dashboard and transforming it for an AI agent are not the same work. Here's the five-step preprocessing chain that makes your data agent-ready.

Data Profiling

Data quality assessment
Schema analysis
Completeness scoring
Relationship mapping

Pandas Profiling OpenRefine Deequ

Data Cleaning

Handle missing values
Remove duplicates
Resolve outliers
Entity resolution

OpenRefine Pandas Statistical IQR

Data Reduction

Noise reduction
Dimensionality
Feature selection
Performance tuning

Scikit-learn NumPy TensorFlow

Transformation

Normalization
Standardization
Encode categorical
Semantic enrichment

Spark SpaCy NLTK

Feature Engineering

Create new features
Feature selection
Embedding prep
Context enrichment

Feature-engine MLxtend Pandas

The output of preprocessing A knowledge source that serves as an external dataset to enhance LLM capabilities, clean, structured, semantically enriched data that agents can retrieve, reason over, and act on with confidence.

Kyklos360 · Transform · Documents

RAG Infrastructure: Making Documents Queryable

Most enterprise knowledge lives in documents (contracts, reports, manuals, policies). RAG (Retrieval-Augmented Generation) is the architecture that makes all of it accessible to AI agents. Without it, agents are blind to everything outside your structured databases.

Prompt + Query

User or agent sends a question requiring knowledge from your documents

Search Knowledge Source

Vector search retrieves semantically relevant chunks from your indexed documents

Enhanced Context

Retrieved information is injected into the prompt as grounded context

LLM Reasoning

The model reasons over the enriched context, not hallucinated knowledge

Grounded Response

Accurate, sourced answer based on your actual data, not the model's training

Vector Search & Embedding

Documents chunked, embedded, and indexed for semantic retrieval. The quality of embedding determines the quality of what the agent retrieves.

FAISS Pinecone Sentence-BERT OpenAI Embeddings

Generative Model Layer

We select and configure the right LLM for your use case, balancing cost, latency, accuracy, and data privacy requirements.

GPT-4o Claude T5 BART Llama

RAG Framework

The orchestration layer that coordinates retrieval and generation, handling chunking strategies, reranking, context window management, and tool use via MCP.

LangChain LlamaIndex LangGraph

Efficient Indexing & Retrieval

High-performance retrieval infrastructure that scales with your document volume, from hundreds of PDFs to millions of records.

Elasticsearch Weaviate Milvus pgvector

Pipeline Orchestration

End-to-end workflow management, keeping embeddings fresh, coordinating multi-step agent flows, connecting agents to tools via MCP, and handling failures gracefully.

Apache Airflow LangChain Kafka n8n

Real-Time Monitoring

Continuous evaluation of retrieval quality and output accuracy. User feedback loops that improve agent performance over time.

Kibana LangSmith Arize Ragas

Scope & boundaries

What TekCapitol does.
What we don't.

Clarity on scope prevents bad engagements for both sides.

✗ We don't

✗ Replace your existing data platforms, Snowflake, Databricks, Salesforce stay

✗ Sell you new software licenses or take platform commissions

✗ Run 18-month engagements with 20-person teams and slide decks

✗ Build AI models from scratch, we work with existing foundation models

✗ Overpromise ROI before understanding your actual data environment

✓ We do

✓ Build the data readiness layer on top of platforms you already own

✓ Start with a fixed-scope assessment before any implementation commitment

✓ Deliver working AI workflows, not strategy documents

✓ Build governance and monitoring in from day one, not bolted on later

✓ Tell you honestly if your environment isn't ready, before you spend more

The data infrastructure layer that determines whether your AI agent reaches production — or stalls trying.

Data readiness on your existing stack

Data Orchestration: Two Layers,One Coordinated System

Data Preprocessing for AI

RAG Infrastructure: Making Documents Queryable

What TekCapitol does.What we don't.

Data Orchestration: Two Layers,
One Coordinated System

What TekCapitol does.
What we don't.