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A closer look at projects where AI and automation reduced manual work, improved quality, and helped teams change how they operate.

Legal

Agentic settlement platform for TRO defense matters

A law firm needed a platform to manage Schedule A TRO defense and settlement matters from intake through negotiation, agreement review, payment, dismissal tracking, and client updates.

Challenge

The work combined legal judgment, evidence collection, IP infringement analysis, settlement valuation, plaintiff-counsel negotiation, document generation, agreement review, and multilingual client communication. A simple drafting assistant would not be enough.

Approach

Built an end-to-end case workflow with client intake, lawyer dashboard, client portal, document collection, status tracking, and workflow modules.

Integrated case collection and verification so docket data, court status, plaintiff information, and case context can feed the workflow.

Added legal-opinion preparation and IP analysis support, including evidence extraction and product-image handling for copyright, trademark, and design-patent matters.

Automated negotiation support with settlement calculations, plaintiff-evidence extraction, Rule 408 outreach, counter-offer argument selection, and lawyer-reviewed email drafting.

Built settlement agreement review against a legal checklist covering amount, release, no-admission language, account identification, supplier disclosure, payment terms, and unfreeze obligations.

Connected follow-up steps including payment forwarding, restoration monitoring, voluntary dismissal tracking, and case closing.

Results

Workflow coverage

End-to-end

Intake to closing

Connected modules

Analysis, negotiation, review, payment, monitoring, closing

Lawyer oversight

Built in

Review gates at legal decision points

Turned a multi-step legal service into a managed agentic workflow with lawyer oversight at the points that matter.

Reduced reliance on scattered templates, inbox memory, and manual status tracking.

Created a platform foundation for faster settlement negotiation and more consistent case handling.

Client Voice

"The platform is designed around the whole negotiation lifecycle, not just email drafting."

Fintech

AI-assisted accounting-data ingestion and account mapping

A fintech needed to ingest accounting exports from different systems and convert them into a common data model for analysis, comparison, and downstream processing.

Challenge

Client accounting files arrive in many layouts, with different headers, merged fields, section dividers, account names, date formats, and reporting conventions. A single prompt was too slow, too costly, and too unreliable for large transaction files.

Approach

Designed a universal ingestion algorithm that splits the problem into specialized agent tasks instead of asking one model to transform an entire file.

Used agents to identify headers, merged columns, date columns, description fields, value columns, transaction rows, section dividers, and remaining fields.

Combined AI decisions with deterministic checks such as column-format fingerprinting, exact matching, confidence scoring, and exception alerts.

Added semantic similarity for account-name standardization, with a matching table that improves as human-reviewed mappings accumulate.

Designed the solution as a Python API packaged in Docker, with deployment options for cloud or client-controlled infrastructure.

Results

Target file success rate

95%

With 100 sample accounting-package formats

Target processing speed

<2 min

For most supported file formats

Human review

Exception-based

Confidence scores trigger review when needed

Moved the solution away from fragile one-shot prompting toward a scalable agent-and-algorithm architecture.

Created a human-in-the-loop matching process designed to become more automated as the proprietary matching table grows.

Established a path for processing complex Excel and CSV files with alerts and confidence levels when human review is needed.

Client Voice

"The value was not just using AI. It was turning messy accounting files into a repeatable ingestion and mapping workflow."

Legal

From document automation to managed legal workflow

A law firm filing thousands of arbitration cases needed to turn repetitive matter data into reliable document packs without scaling the legal team linearly.

Challenge

The work was not only drafting. The firm needed a controlled process for collecting client information, mapping that information into the right templates, handling multilingual inputs, and deciding when human review was required.

Approach

Reframed the project as a document-operations system rather than a simple drafting prompt.

Separated the workflow into data collection, template population, exception handling, quality review, and user training.

Built a local Windows application with model choices based on document length, quality requirements, and cost.

Added cost transparency and review checkpoints so lawyers could decide when automation was appropriate and when manual review was needed.

Results

-80%

Document generation time

Reduction after automation

25%

Staff increase avoided

Planned hiring no longer required

3 wks

Prototype timeline

First working prototype

80% reduction in document generation time.

Avoided a planned 25% increase in staffing.

First prototype ready in 3 weeks, with lawyers actively using it 2 weeks later.

Client Voice

"The first prototype was ready in 3 weeks, and my lawyers were actively using it 2 weeks later."

Banking

Control remediation with structured quality checks

A bank's operational risk department managed 5,000 controls and needed to improve the quality, consistency, and usefulness of its control descriptions.

Challenge

Many descriptions were too vague to be useful. The problem was not simply rewriting text, but enforcing a repeatable control-description standard across a large control inventory.

Approach

Converted the bank's quality expectations into structured review criteria.

Used language AI to draft improved descriptions while preserving the underlying control intent.

Added review logic around the three elements every description needed: what is controlled, what issue is identified, and what remediation or escalation follows.

Optimized the workflow for batch processing so reviewers could focus on exceptions instead of starting each rewrite from scratch.

Results

87%

Descriptions meeting standard

After AI-assisted remediation

5,000

Control inventory

Controls in scope

40%

Weak descriptions identified

Unclear or below standard at assessment

87% of rewritten controls met the technical requirements for proper control descriptions.

Improved clarity, context, acronym expansion, and remediation language across the rewritten controls.

Reduced the amount of manual rewrite work needed from risk specialists.

Client Voice

"The team's expertise in financial services and AI was instrumental in transforming our control descriptions."

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