The estimating software behind every defensible commitment.

SEER ships with models calibrated against four decades of completed program data, so organizations can produce defensible estimates before their own historical data is mature. As outcomes are connected back to estimates, SEER calibrates to the specific environment and accuracy compounds over time.

SEERai is auditable and explainable by design. Every assumption SEERai generates is traceable to its source, human-reviewed, and approvable at each decision point. AI accelerates the preparation. People own the commitment. There is no black-box forecasting.

Spreadsheets and internal models start fast but are fragile, person-dependent, and hard to defend at scale. Lightweight estimation tools handle one task, not cost, schedule, labor, and risk together. Consulting firms deliver an estimate, not a repeatable capability the organization owns. Engineering-analytics and AI-native planning tools report on work already underway, while SEER models the commitment before execution begins on validated parametric models with a traceable audit trail. SEER complements ERP and PLM rather than replacing them.

SEER replaces fragile, person-dependent spreadsheets with a governed, reviewable basis for commitment. Spreadsheets lack validated parametric models, an audit trail behind every number, and institutional memory that survives turnover. SEER keeps the speed of spreadsheet-based estimation while adding the defensibility, traceability, and calibration that spreadsheets cannot provide.

SEER is modular and configurable. Organizations license the domains they need, such as hardware, software, manufacturing, or IT, and scale across teams as adoption grows rather than paying for the full platform up front. SEER is used on the largest mission-critical programs in the world and by mid-market engineering teams. Pricing is scoped to each organization through a consultation.

Teams save an average of 11 days per project on estimation preparation alone. Customers have reported cost avoidance in the hundreds of millions on individual programs. Galorath scopes a pilot to demonstrate measurable value before a full rollout.

Galorath provides onboarding, calibration support, training, and ongoing advisory services, including industry-specific implementation expertise and support for global, distributed teams. Formal training and certification through SEER University make adoption durable across departments.

SEER is typically adopted in phases, starting with a pilot on a live program to prove value, then standardizing across teams. Because every estimate is transparent and traceable, SEER earns trust with engineers and program managers instead of overriding their judgment, which makes standardization and executive sponsorship easier to secure.

SEER supports both Agile and traditional delivery. SEER sizes software objectively, models effort and capacity, and feeds a top-down check into story-point planning rather than replacing it. Agile teams gain a defensible, parametric counterpart to bottom-up estimates.

SEER does not require dedicated cost engineers or a formal PMO. SEER ships with validated, pre-calibrated models, so teams without deep cost-engineering experience can produce reliable estimates from the start. SEERai reduces the learning curve further by interpreting requirements and structuring inputs automatically. Dedicated estimators and a mature PMO make the platform more powerful but are not a prerequisite.

SEER is built for cross-functional use, not only specialists. Estimators get the full depth of parametric modeling, while executives and program leaders get clear dashboards, ranges, and reporting they can act on without reading the underlying model. Technical rigor never comes at the cost of accessibility.

SEER scales from single programs to enterprise portfolios. Smaller teams license only the domains they need and use SEER to bring rigor and defensibility to high-stakes decisions without standing up a full PMO. The same validated models that serve the largest programs work at smaller scale.