InsightSolTechnologies
Service 02 of 10

AI and Data Innovation: Machine Learning, RAG and Analytics

Most companies are not short of data. They are short of a path from the data they have to a decision someone can act on. The warehouse exists but nobody trusts the numbers, the model lives in a notebook, and the LLM pilot answered questions in a demo and hallucinated in the field. Each of those is an engineering gap, not a research problem.

InsightSol closes that gap end to end. We build the data foundation first, so every model and dashboard reads from one governed source. On top of it we ship machine learning models with monitored accuracy, retrieval-augmented LLM applications that cite the documents they answer from, and analytics that executives open because the figures reconcile with finance.

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PythonPyTorchscikit-learnpandasdbtApache AirflowPostgreSQLpgvectorClaude APIOpenAI API

In brief

What does AI and data innovation cover at InsightSol?

It covers the full path from raw operational data to a decision: a governed data foundation, machine learning models with measured accuracy, retrieval-augmented LLM applications that cite their sources, and analytics that reconcile with finance, all served and monitored in production.

  • One governed data model in dbt, tested on every load, that every model and dashboard reads from
  • Models evaluated on held-out data against a threshold agreed before training, with drift alerts in production
  • LLM applications that answer only from retrieved, cited passages and say "not found" rather than guess
  • Typed serving endpoints with cost per call and scheduled re-evaluation, operable by your own team

Who this is for

  • Leadership teams whose dashboards disagree with finance and whose decisions wait on a manual report
  • Product teams adding an LLM assistant or search over their own documents and data
  • Operations teams with a forecasting, scoring, or classification problem a model can take over

What you receive

  • Data model in dbt with tests, documentation, and lineage for every table
  • Feature and embedding pipelines on Airflow with monitoring
  • Trained model with an evaluation report against the agreed threshold
  • Retrieval-augmented LLM application with citations and a "not found" path
  • Typed serving endpoints with drift detection and cost tracking
  • Dashboards your executives open, reconciled with finance

Architecture pattern

How the system is put together.

The components we ship, in the order a request moves through them. Every boundary is typed, every failure path is designed, and every action is logged.

  1. 1

    Governed data foundation

    Ingestion from your operational systems into PostgreSQL or a cloud warehouse, modeled with dbt, versioned, tested, and documented so every downstream consumer reads the same definitions.

  2. 2

    Feature and embedding pipelines

    Scheduled jobs on Airflow compute features for models and embeddings for retrieval, stored in pgvector with lineage back to the source record.

  3. 3

    Model training and evaluation

    Classical machine learning on scikit-learn or gradient boosting where it wins, deep learning on PyTorch where it is needed, with held-out evaluation sets and acceptance thresholds agreed before training starts.

  4. 4

    Retrieval-augmented LLM layer

    Chunking, hybrid search, reranking, and prompt assembly that returns a cited answer, with a fallback to "not found" instead of a confident guess.

  5. 5

    Serving, monitoring and MLOps

    Models and retrieval endpoints behind typed APIs, with drift detection, cost per call, and scheduled re-evaluation, so accuracy is a chart your team watches rather than a complaint from a user.

Failure prevention

Tested data contracts

Schema and freshness tests run on every pipeline load, so a broken upstream export stops the pipeline instead of poisoning the model.

Held-out evaluation

Accuracy is measured on data the model never saw, against a threshold written down before training, so a model that memorized the training set does not ship.

Cited answers only

The LLM layer returns the passages it answered from and says "not found" when retrieval is weak, which is what makes the output auditable.

Drift alerts

Input distributions, prediction distributions, and retrieval hit rates are tracked over time and alert before accuracy degrades.

Targets we design to

  • 1

    governed source of truth for every model and dashboard

  • 100%

    of LLM answers traceable to a cited source passage

  • <24h

    from data landing to feature availability for models

How the engagement runs

From audit to handover.

AI and data work starts with the audit: we inventory the sources, measure data quality, and run proof experiments against a sample of real records to confirm the model or retrieval approach reaches the accuracy the use case needs. The data foundation ships first, because every later deliverable depends on it. Models and LLM layers then ship in phases, each gated by its evaluation set, and your analysts take over the dashboards as they go live.

See the audit process

Questions about this service

Not a large one. You need one governed source the model reads from, which can be a well-modeled PostgreSQL database for most mid-sized companies. We size the foundation to the use case and move to a cloud warehouse only when the volume or the number of consumers justifies it.

Retrieval first, generation second. The model only answers from passages the retrieval layer returned, every answer carries its citations, and when retrieval confidence is low the system says it could not find an answer. We measure the hallucination rate on a test set before launch and track it in production.

Yes, and that is the common case. We usually take the engineering side: pipelines, serving, monitoring, and evaluation harnesses, so your scientists spend their time on models rather than on plumbing.

In production

Case studies built on this discipline.

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A multi-agent outbound engine that researches, qualifies, personalizes, and books, with human sales reps reviewing exceptions, not running the process.

Read the case study

Construction bidding and earthwork

99.4%

pipeline uptime across a full production quarter

Real-Time Geospatial and Earthwork Estimation Pipeline

An event-driven pipeline that ingests site plans and geospatial data, computes cut-and-fill volumes, and produces traceable, bid-ready estimates.

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Ready to put your data to work?

Book an architecture call. We will map the workflow, the systems it touches, and the number this discipline should move for you.

Book an architecture callusman@insightsol.net
  • No-obligation call
  • Architecture first, build second
  • You own all the code