NexGen Solutions
NexGen Solutions
AI Integration · MLOps · Delivery
AI Integration Practice

AI wired into thesystems you already run.

End-to-end AI delivery, from your data to a monitored system in production. No orphaned proofs of concept.

Explore capabilities
  • On-prem or cloudYour data stays where you need it
  • Any modelSwap vendors by config
  • Built for auditLogged, explainable, governed
  • Yours to runHanded over, not locked in
Any model, swappableGuardrails · monitoring · auditYour AI layerDocumentsDatabasesApps & APIsCalls & audioAnswersForecastsAutomationsAlertsWHAT YOU RUNWHAT YOU GET

Contracts and policies in, answers with the source cited.

Capabilities

Six practice areas, one delivery standard

Pick one from the list or swipe through the cards to see it working. All six have shipped into production, not just into a slide.

Large Language ModelsLive
Which contracts auto-renew this quarter?RETRIEVED FROM YOUR FILESMSA_Acme.pdf1Supply_Beta.pdf2Lease_Cobalt.pdf1Search by meaning2Ground in the text3Answer and citeAcme and Beta both renew before 30 Sep;Beta needs 60 days' notice.[1][2]Every answershows its source →
Computer VisionIdle
LIVE INSPECTION · LINE 2Runs on-siteOK 0.99OK 0.98Defect 0.94OK 0.99OK 0.97PassFlagged for reviewImages never leave the building
Predictive AnalyticsIdle
DEMAND FORECASTHistoryForecastLikely rangePlausible rangeWHAT DRIVES ITSeasonalityPricePromotionsWeather
MLOps & DeploymentIdle
ModellifecycleTrainVersionDeployMonitorRetrainLIVE ACCURACYAlert thresholdDrift detectedRetrained, accuracy restored
Data Pipelines & ETLIdle
CRMERPFilesEventsIngest & testSchemaNullsDupesWarehouseone source of truthModelsBIAppsScheduled, tested on every run, lineage tracked end to end
Voice AI & SpeechIdle
LIVE CALLCALLERI've moved and need to update my address.VOICE AGENTNo problem. What's the new postcode?UNDERSTOOD IN REAL TIMEIntentUpdate addressSentimentNeutralNext stepVerify identity

LLMs · RAG · NLP

Illustrative data

Large Language Models

Ask your own documents and data a question, and get an answer that shows its sources.

  • Search by meaning

    Retrieval over contracts, policies and wikis, not keyword guessing.

  • Answers you can check

    Every response cites the passage it came from.

  • Local or frontier

    Ollama on your hardware, or Claude, GPT and Gemini via API.

RAGFine-tuningAgentsEvaluation
How RAG worksAsk Your Data

Delivery

Built to reach production

Most AI work stalls after the demo. Ours is planned around everything that comes after it.

87%

of machine-learning projects never reach production, by common industry estimates.

Ships Stalls

A typical AI project

  1. Idea
  2. Prototype
  3. DemoStalls after the demo

With NexGen

  1. Assess
  2. Prepare data
  3. Build
  4. Deploy
  5. MonitorLive, owned by your team

The difference is unglamorous: data that holds up, a deployment path agreed on day one, monitoring that catches drift, and a team on your side that can run it without us.

  1. 1

    Assess

    Find where AI pays off, and where it doesn't.

    You get: Readiness report
  2. 2

    Prepare data

    Clean, governed pipelines the model can rely on.

    You get: Tested data pipeline
  3. 3

    Build

    Models benchmarked against a simple baseline.

    You get: Validated model
  4. 4

    Deploy

    Into your stack, on-prem or in your cloud.

    You get: Live service
  5. 5

    Monitor & hand over

    Drift alerts, docs, and your team trained to run it.

    You get: Runbook + training

Technology

The stack, layer by layer

What we build with, from the model at the top to the hardware underneath. Knowing which to reach for is the actual work.

  • Models

    Swappable by config

    ClaudeOpenAIGeminiMistralLlamaOllamaHugging Face

  • ML & frameworks

    PyTorchTensorFlowKerasscikit-learnONNXLangChainOpenCVElevenLabs

  • Retrieval

    pgvectorQdrantMilvusElasticsearchFAISS

  • Data

    SnowflakeDatabricksPostgreSQLSparkKafkaAirflowDuckDBPolarspandas

  • Ship & operate

    PythonTypeScriptFastAPIDockerKubernetesTerraformGitHub ActionsMLflowW&BPrometheusGrafana

  • Compute

    AWSAzureGoogle CloudNVIDIAYour servers

Model providers are interchangeable by design: every integration sits behind an abstraction, so a change of vendor is a config change, not a rewrite.

See it in action

Integration you can try before you buy.

Running examples of the pipelines we wire into production stacks.

Open sourcePDF → Structured CSVUnstructured documents into one tidy schema: the extraction pattern behind most integrations.Open sourceAsk Your DataLLM agents writing read-only SQL over your tables: plain-English questions in, answers and charts out.Open sourceNRC Technical Letter SearchGrounded RAG over regulatory reports, every claim backed by a quote verified against its source page.Playground demoHow RAG WorksVector search over your own text, step by step: the retrieval layer we deploy on-premise.All open-source projects →

A thirty-minute readiness assessment, no deck required.

If AI isn't the answer yet, we'll say so. Prefer to judge the engineering first? Browse our open-source work.

  • 1We look at your data
  • 2We name the highest-value opportunities
  • 3You get a route to production