Solutions
Talent, teams and solutions for your software work
Add engineers to your own team, hand a workstream to a managed team or have us deliver a defined project. Further down: six common problems and how we solve them.
Ways to work with us
Choose how you want to work with us
Each option has its own page covering who it suits, how it works, what you get and how it compares with the others.
Talent Solutions
Skilled engineers and specialists who join your team, matched to the work your roadmap needs.
Team Solutions
A complete team that runs a workstream for you, while you stay involved in the decisions.
Project Solutions
A defined outcome delivered from scope to handover, with one team accountable for the result.
International Talent Solutions
Skilled people from beyond your local market, with working hours and handovers agreed up front.
Common problems
Software, cloud and data problems we solve
Each section below takes a common problem and sets out how we approach it, what changes once it’s solved and which of our services are involved.
Legacy modernization
Retire legacy systems one piece at a time
Modernize critical systems in reversible steps while feature work continues.
The challenge
The systems behind billing, claims or fulfillment often hold years of business rules nobody wrote down, on a stack that gets harder to patch, host and hire for. A full rewrite looks clean on paper, but it usually means a long feature freeze, two systems to maintain and one high-stakes cutover.
Our approach
- Map modules, integrations, data flows and the business rules buried in the code
- Decide for each component whether to move, rebuild, replace or retire it, in a sequenced roadmap
- Pin down current behavior with characterization tests before any code changes
- Apply the strangler fig pattern: route traffic through a facade and move capabilities one at a time
- Migrate data in stages with reconciliation checks, then switch off what has been replaced
What changes
- Feature work that keeps moving while the migration runs
- Steps small enough to roll back if behavior differs
- Business rules documented and tested, not held in one person’s head
- A platform your team can hire for, change and operate
Document automation
Stop rekeying data from documents by hand
Extract and check data from invoices, forms and claims, and send anything uncertain to a person.
The challenge
Invoices, claims, applications and contracts still arrive as PDFs, scans and email attachments, and someone has to read each one and key the details into another system. Template-based capture tools break when a supplier changes its layout, so exceptions pile up, backlogs grow with volume and mistakes turn up later in finance or claims reports.
Our approach
- Build a test set from a labeled sample of your documents, including poor scans
- Classify each document, then extract fields with OCR, layout analysis and language models
- Validate every value against business rules and your systems of record
- Route low-confidence fields to a reviewer, and turn their corrections into new test cases
- Post approved data to ERP, claims or case systems through APIs, and track accuracy in production
What changes
- Staff handling exceptions instead of retyping every page
- Values that break a business rule stopped before posting
- Every extracted value traceable to its source document
- Accuracy reported for your own documents rather than a vendor’s samples
Knowledge assistants
Find answers buried in wikis, drives and tickets
Assistants that answer from your documents, cite sources and respect permissions.
The challenge
The answer to an internal question often already exists, in a policy, a manual, a runbook or an old ticket. Each sits in a different tool with its own search box, so people interrupt a colleague or give up. A generic chatbot does not fix this: it cannot see your content, it can sound confident while being wrong, and a careless setup can expose documents to the wrong people.
Our approach
- Connect the sources that matter, keep the index in sync and carry over each document’s access rules
- Combine keyword and vector search with re-ranking, so exact codes and paraphrased questions both match
- Filter every search by the user’s permissions, and decline to answer when the sources are silent
- Answer inside the intranet, chat tool or app people already use, citing the sources behind each answer
- Score answers against real questions before launch and after every prompt, model or index change
What changes
- Answers people can verify against the original document
- Fewer routine questions landing on experts and team leads
- No new way around your document permissions
- An evaluation set that shows whether each change made answers better or worse
Cloud foundations
Take control of cloud sprawl, spend and access
A landing zone, infrastructure as code and guardrails, so workloads start secure.
The challenge
Cloud accounts often grow one project at a time: environments assembled in the console, broad admin access, inconsistent tagging and a monthly bill nobody can fully explain. Each new team rebuilds networking and pipelines from scratch, security reviews queue up because nothing is standard, and workloads waiting to migrate have no agreed place to land.
Our approach
- Assess accounts, networking, identity, spend and security against a written baseline
- Design the landing zone (account structure, networks, identity, logging) and define it as code, typically in Terraform
- Enforce guardrails and tagging as policy as code, with budgets and alerts for each team
- Give teams golden-path templates for compliant environments, services and pipelines
- Prepare the platform for migration waves, then hand over with runbooks and dashboards
What changes
- Infrastructure changes reviewed like application code
- Spend you can trace to a team, product or environment
- Security baselines applied by default, not checked after the fact
- Teams starting new services without waiting in an ops queue
Release confidence
Release more often without breaking production
Test automation, CI/CD and gradual rollouts that make a release routine rather than an event.
The challenge
When each release needs a change window and a round of hand testing, teams batch work into large, infrequent deployments where one defect can hold up everything. Unreliable tests are rerun until they pass, hotfixes bypass the process, and breakages come to light only after release.
Our approach
- Trace the path from commit to production, and start measuring lead time and change failure rate
- Shift testing toward fast unit and contract tests, backed by a small end-to-end suite
- Quarantine flaky tests and fix what makes them flaky
- Automate builds, deployments and short-lived test environments in one CI/CD pipeline
- Roll out behind feature flags or canary releases, with a rollback path tested in advance
What changes
- Smaller, more frequent releases that are easier to review and test
- More regressions caught in the pipeline, before customers see them
- A known, tested way back when something does slip through
- Release decisions based on pipeline evidence, not gut feel
Data platforms
Make reporting and AI run on data you trust
Tested pipelines, a governed warehouse or lakehouse and metric definitions every team shares.
The challenge
Data sits in operational databases, SaaS tools and spreadsheets, stitched together by scripts and exports that few people understand. Reports disagree because teams calculate the same metric differently, pipelines fail without anyone noticing, and AI projects stall on data that is incomplete, undocumented or not cleared for that use.
Our approach
- Inventory sources, owners and the decisions each dataset supports
- Build ingestion and transformation pipelines as tested, version-controlled code
- Model core entities and metrics once, with definitions the business signs off on
- Set access, masking and retention rules that match how each dataset may be used
- Monitor freshness, quality and lineage in production, and alert owners when a load fails
What changes
- One agreed definition for each metric that matters
- Pipeline failures flagged to their owners, not found later in a report
- Datasets documented well enough for analysts and AI projects to reuse
- Clear ownership and access rules for sensitive data
Next step
Your problem doesn’t have to fit a category
Describe what you are dealing with and what you have tried so far, and we’ll propose which services to combine and in what order.
Or call +1 302-464-5943 · Eastern Time (ET)