ECLACTRA™'s AI and data engineering solutions take a model from a raw feed to a decision a business can rely on: machine learning, computer vision, natural language processing, and generative and agentic AI, built on data engineering and MLOps foundations that keep a model running in production instead of stuck in a notebook.
Jump straight to the capability you need, or read the full pillar below.
From a question nobody can answer to a model in production answering it.
Most AI programs stall between the pilot that impressed everyone and the system anyone actually uses. Our AI and data science consulting covers both halves: the statistical work to prove a model earns its place, and the engineering to put it somewhere it changes a decision.
How we engage
Model development that survives contact with real data.
Algorithm selection, training, and tuning is its own discipline, separate from the platform that later runs the model in production. Our machine learning engineering practice builds and validates the model itself, classical and deep learning alike, before it ever reaches MLOps.
How we engage
Teaching a camera to catch what a person would miss on the tenth pass.
Object detection, quality inspection, and OCR built for the conditions your cameras actually operate in (poor lighting, motion, occlusion), not a clean benchmark dataset. Deployed to edge hardware where a round trip to the cloud is too slow to matter.
How we engage
Structure out of the text nobody has time to read.
Classification, entity extraction, and sentiment analysis over documents, tickets, and transcripts too high-volume for manual review, built as a distinct discipline from generative AI, with evaluation against labelled ground truth rather than a subjective read of the output.
How we engage
LLM systems that answer from your data, not the model's guess.
Retrieval-grounded LLM systems, drafting and copilot tools, and content generation pipelines, built with citations back to source, human review at the escalation points that matter, and an evaluation harness that catches a regression before your users do.
How we engage
Autonomous agents for the decisions that are routine, not the ones that aren't.
Multi-agent systems that execute defined, bounded workflows (triage, research, routing, reconciliation), with human approval built into every escalation point, not bolted on after an agent does something it shouldn't have.
How we engage
The right item in front of the right person, measured, not guessed.
Personalization and recommender engines built against a business metric (conversion, retention, engagement), not just a similarity score, with the cold-start and feedback-loop problems handled explicitly rather than discovered in production.
How we engage
The unglamorous 80% of a model's performance nobody budgets for.
Cleaning, joining, and transforming raw data into features a model can actually learn from, plus the feature stores and pipelines that keep training and serving data consistent, so a model's production accuracy stops silently drifting from its offline number.
How we engage
Trustworthy data, and systems that were never designed to talk actually talking.
Batch and streaming pipelines into a warehouse or lakehouse, and the governed integration layer that replaces brittle point-to-point scripts nobody wants to own. It comes with quality tests, lineage, and a catalog, so a number has one definition and one owner.
How we engage
The unglamorous layer that decides whether your models survive contact with production.
A model that cannot be retrained, versioned, rolled back, or explained is a liability with good accuracy numbers. Our MLOps consulting builds the platform underneath your models: reproducible training, a registry, evaluation gates, and drift monitoring.
How we engage
Engagements in this pillar are delivered on named platforms we build and run, not a fresh stack every time.
The knowledge and agent layer that turns enterprise data into action.
Launching soonForecasting, analytics, and dashboards built around a decision.
Launching soonWhere the work is repeatable enough to publish a floor, we publish it. These are starting prices for a scoped engagement, not an hourly rate you find out about later.
Trustworthy data, and systems that were never designed to talk actually talking.
from$22,000per engagement
LLM systems that answer from your data, not the model's guess.
from$28,000per engagement
Teaching a camera to catch what a person would miss on the tenth pass.
from$34,000per engagement
From a question nobody can answer to a model in production answering it.
from$26,000per engagement
Model development that survives contact with real data.
from$30,000per engagement
Structure out of the text nobody has time to read.
from$24,000per engagement
Autonomous agents for the decisions that are routine, not the ones that aren't.
from$32,000per engagement
The right item in front of the right person, measured, not guessed.
from$27,000per engagement
The unglamorous 80% of a model's performance nobody budgets for.
from$18,000per engagement
The unglamorous layer that decides whether your models survive contact with production.
from$36,000per engagement
Every figure is a starting point, not a quote. The scope, the timeline, and the final number are agreed on a call before anything is committed, and the capabilities without a published price here are the ones that vary too much to put one on honestly.
Most engagements draw on more than one. They're built as a single stack, not six separate practices.
One live operating picture, GeoAI on top of it, and the field tooling to act on what it shows.
6 capabilitiesFrom a validated idea to a shipped product, and the platform engineering that lets it scale.
5 capabilitiesSimulation, field overlays, and shared spatial sessions for work that's expensive to get wrong.
6 capabilitiesNamed executives, embedded and accountable, matched to the leadership gap that's costing you most.
5 capabilitiesThe platform, infrastructure, and frontier technology layer underneath everything else we build.
4 capabilitiesCan't find what you're looking for? Ask us
Not a finished one, but you need the feeds the model depends on to be reliable. Data engineering and MLOps are capabilities inside this pillar precisely because most stalled AI programmes are actually stalled data programmes, so we build the two together rather than waiting on one.
Book a 30-minute strategy session with a ECLACTRA™ lead. No sales deck, just a straight conversation about where AI, geospatial, engineering, or fractional leadership could actually move the needle.