Your legacy system is blocking your AI roadmap.

Different buyer trigger, different engagement shape, same engineering team.

The AI-Driven Application Modernization engagement (this page) is driven by a desire to ship production AI features. The modernization is structured around AI readiness from day one: API-first architecture, structured data pipelines, and AI capability integration delivered alongside the architectural work.

The Legacy Application Modernization engagement is driven by support sunsets, hiring scarcity, compliance findings, or infrastructure spend. The output is a modernized system on a supported stack with a hireable talent pool. AI-assisted engineering shows up as the method we use to deliver, not the product. If your driver is one of those, that engagement is the right starting point.

Both are common situations. Most teams arrive with at least one prototype that worked in a demo and broke when production traffic hit it. The assessment determines whether the prototype's pattern is right and just needs production scaffolding (data pipelines, observability, evaluations), or whether the architecture forces a rebuild.

We keep what is reusable. Prompts your team has tuned, embeddings you have already generated, evaluation datasets you have validated, and integration code that works with your existing stack stay. The work focuses on the parts that genuinely require engineering attention to survive production, not on rewriting work that is already sound.

Frontier model families (Claude, OpenAI, Gemini), open-source models served via managed inference, and the cloud-managed equivalents (Azure OpenAI Service, AWS Bedrock, Google Vertex AI). The orchestration layer covers retrieval-augmented generation pipelines, agent frameworks, evaluation harnesses, and structured-output enforcement.

Model choice depends on data residency requirements, compliance constraints, and inference economics, not on vendor preference. We benchmark the candidate set against your workload before locking in the design, and we revisit the choice as the model landscape moves. The architecture is built so the model layer is replaceable without rewriting the application above it.

Data is part of the assessment, not a separate workstream. We map what you have: where it lives, what shape it is in, what governance applies, and what is actually usable for retrieval, embedding generation, fine-tuning, or agent context. The output of Phase 1 includes a data-readiness section alongside code-readiness, with the gap between "data exists" and "data an LLM can use" named explicitly.

Phase 2 includes the pipeline work to get structured data flowing into the patterns your AI use case requires: vector indices, retrieval layers, structured caches, and evaluation datasets. The result is data infrastructure that survives the first AI feature and supports the next four, not a one-off integration that has to be redone for every use case.

Source code confidentiality is contractual before any code is shared, and engineer-level visibility is limited to the assigned team and revoked at engagement close. That part of the answer is the same as any modernization engagement.

For data flowing into LLMs, we use deployment patterns that keep your data inside your boundary: model endpoints hosted on your cloud account (Azure OpenAI, AWS Bedrock, Vertex), private inference where the policy requires it, opt-outs of training-data retention, masking and tokenization of sensitive fields before any prompt is constructed, and audit logging on every model invocation. The handling pattern is sized to your compliance environment, not the other way around.

Yes, we have run engagements under each of those regimes. The pattern starts with restriction-aware architecture: which model endpoints are permitted, what data is allowed to leave the regulatory boundary, and what audit evidence is required for sign-off.

For HIPAA, we use BAA-covered model endpoints and de-identification before any model invocation. For GDPR and India DPDP, we restrict to in-region inference and produce a data-flow map for DPO sign-off. For SOC 2, the audit logging and access-control patterns are designed against your existing control set, not bolted on afterwards. The compliance map is part of the assessment, not a discovery item raised during build.

Yes, and we often recommend it. A scoped first engagement on one high-value AI use case (intelligent document processing, agentic workflow, retrieval-augmented assistant, automated decision support) on a single isolated module proves the working relationship before larger commitment. The module suitable for a first engagement is identified during the assessment based on isolation potential, AI value, and migration effort.

The architectural choices for the first module are designed to extend, not to be redone, when the next module or use case is taken on. The first engagement produces a production result, not a prototype, even when the scope is intentionally small. Stopping after the first module still leaves you with a working, supported AI capability, not a stranded migration.

Two specific things. First, AI-generated regression test scenarios derived from the existing behavior of the legacy system, used to verify parity between the legacy and the modernized component during cutover. Coverage is reviewed by senior engineers, not accepted as-is.

Second, evaluation harnesses for the AI capabilities themselves: structured test sets, regression evaluations on prompts and agents, output-quality scoring against reference answers, and red-team scenarios for adversarial inputs. AI features without evaluations cannot be safely changed, and changes are inevitable as models and prompts evolve. Both deliverables are validated and handed off in Phase 3 so your team can run them without us in the room.

Inference spend is sized during Phase 1 against your expected traffic, model mix, and retrieval volume. The estimate covers per-request inference, embedding generation, retrieval infrastructure, caching layers, and observability tooling. The estimate is reviewed against actual usage in the first 30 days post-launch and re-baselined with your finance team.

Model choice is the major lever. The right answer is almost always a tiered architecture: smaller, cheaper models for routine traffic, frontier models reserved for the requests that genuinely require them, and aggressive caching for repeated queries. We design for that from the start rather than defaulting to the most expensive model for every request, which is the most common reason AI features blow their budget in the first quarter.

Two common patterns. First, a defined hyper-care window immediately after cutover, typically 30 to 90 days, with our engineers on shared channels for production support, prompt and model regression triage, and inference-spend monitoring. This is included in most engagements at no incremental price.

Second, an ongoing AI operations retainer for clients who want us engaged for continuing prompt iteration, evaluation maintenance, model upgrades, and observability work. Both are optional. The base engagement is structured so your team can take full ownership at handoff, with documented runbooks for incident response, evaluation regressions, model rollback, and inference-spend alerting.

Being on a cloud platform is not the same as being cloud-native. Most systems that moved to cloud run as a monolith on a virtual machine, which is the lift-and-shift pattern. Production AI workloads need managed services, event-driven scaling, and clean data pipelines that the lift-and-shift architecture does not provide.

If your existing architecture is monolithic, the AI-driven modernization restructures around API-first design, managed services, and event-driven scaling on your current cloud. Re-platforming you to a different cloud is rarely the right answer, and the assessment will say so. If your existing architecture is already cloud-native, the engagement is mostly AI augmentation, not architectural rework, and the price reflects that.

It happens, and we say so. Sometimes the right answer is a targeted refactor of one component to support a single high-value AI use case, rather than a full AI-driven modernization. Sometimes it is starting with data work (governance, quality, structure) before any AI capability is added, because the data itself is the blocker. Sometimes it is delaying the AI roadmap until a specific compliance or organizational dependency is resolved.

The assessment recommends what the system and the business need, not what is profitable to recommend. If the finding is that AI-driven modernization is not the right next move, you have a written rationale to take to your board, and you have spent nothing beyond the assessment.