Our work covers customer experiences, enterprise applications, mobile products, integration layers, data platforms, and AI enabled workflows.
Discovery, user research, workflow mapping, prioritization, measurable outcomes, and a delivery roadmap that guides design and engineering decisions.
Our value lies in connecting business context with engineering execution. We clarify the outcome, design the experience, build the platform, and establish the practices needed to run it.
We map decisions, handoffs, data, and exceptions behind a workflow, so requirements are concrete, testable, and grounded in real use.
Shared priorities, working demonstrations, and risk reporting let stakeholders steer with a clear view of scope, cost, and timing.
Clear interfaces, automated tests, observability, and documentation mean new features do not require rebuilding the foundation.
Controls such as encryption, least privilege, and secure development practices are planned from the first sprint.
AI is used where it beats a measured baseline, with evaluation, human review, and monitoring built in.
We can own delivery end to end, provide ongoing product capacity, or strengthen an internal team.
Every use case starts with the user problem, the available data, a measured baseline, and the level of human oversight the decision requires.
Creates or transforms content: answers, summaries, drafts, structured extraction from unstructured material
Documents, text, images, and approved knowledge sources
Natural language or structured content that can vary between runs
Accuracy against reference answers, grounding in sources, and rubric based human review
Unsupported or fabricated statements, prompt injection, exposure of sensitive content
Retrieval from approved sources with citations, output validation, content filtering, human approval for consequential outputs
We begin with the user and the outcome, establish an architecture that fits the environment, deliver in reviewable increments, and prepare the product for operation.
Review objectives, users, workflows, data, systems, constraints, and success measures; separate essential capabilities from later enhancements
Product direction, prioritized backlog, assumptions and dependencies, initial risk register
Practices based on OWASP guidance, including the Developer Guide, ASVS, the OWASP Top 10, and the API Security Top 10.
TLS 1.2 or higher, encryption at rest, managed keys, least privilege access, and secrets kept out of source code.
Weekly delivery reviews, demonstrations each iteration, and monthly steering reviews of milestones, budget, and risk.
We select a stack based on product requirements, the client's existing systems and skills, security needs, expected scale, and long term maintainability.
Our management structure is organized around business leadership, technology direction, and operational delivery.
Harunur Rashid brings 19 years of experience in software quality assurance, test automation, and QA leadership.
Sayem Ahmed brings 18 years of experience in software architecture, solution design, and product and delivery leadership.
Asraful Islam brings 16 years of experience in client engagement, project delivery, and bilingual English–Bengali training and documentation.
223 Ashley has delivered software, data, and technology services for organizations including the following.
Engagements range from a focused discovery exercise to a dedicated, long term product team. The commercial model is selected according to the work and the client's operating needs.
Unclear scope or a new product concept
Time boxed deliverables for an agreed fee
Known scope with clear acceptance criteria
Milestones, stated assumptions, and change control
An evolving roadmap requiring ongoing capacity
Agreed team composition, cadence, and monthly terms
A live product needing maintenance and regular releases
Coverage hours, response targets, and a backlog process
A first discussion typically covers your objectives, current systems, constraints, and the engagement model that best fits.
